{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time series forecasting with ARIMA\n",
    "\n",
    "In this notebook, we demonstrate how to:\n",
    "- prepare time series data for training an ARIMA times series forecasting model\n",
    "- implement a simple ARIMA model to forecast the next HORIZON steps ahead (time *t+1* through *t+HORIZON*) in the time series\n",
    "- evaluate the model \n",
    "\n",
    "\n",
    "The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load data only.\n",
    "\n",
    "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import math\n",
    "\n",
    "from pandas.tools.plotting import autocorrelation_plot\n",
    "# from pyramid.arima import auto_arima\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from common.utils import load_data, mape\n",
    "from IPython.display import Image\n",
    "\n",
    "%matplotlib inline\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\") # specify to ignore warning messages\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data from csv into a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2,698.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2,558.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2,444.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2,402.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2,403.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>2,453.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>2,560.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>2,719.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>2,916.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>3,105.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "2012-01-01 00:00:00 2,698.00\n",
       "2012-01-01 01:00:00 2,558.00\n",
       "2012-01-01 02:00:00 2,444.00\n",
       "2012-01-01 03:00:00 2,402.00\n",
       "2012-01-01 04:00:00 2,403.00\n",
       "2012-01-01 05:00:00 2,453.00\n",
       "2012-01-01 06:00:00 2,560.00\n",
       "2012-01-01 07:00:00 2,719.00\n",
       "2012-01-01 08:00:00 2,916.00\n",
       "2012-01-01 09:00:00 3,105.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy = load_data('./data')[['load']]\n",
    "energy.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot all available load data (January 2012 to Dec 2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HNB0ihLikCn3K/PlFS1B+0t7Hww4DdtvNjSykudGtAkYph1w5JFQOCUmIypZ/9Oja5yoM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\niIiIiAjMHBIRERERERGA/wNQA9G0HxP+TAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7beb35f0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create training and testing data sets\n",
    "\n",
    "We separate our dataset into train and test sets. We train the model on the train set. After the model has finished training, we evaluate the model on the test set. We must ensure that the test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n",
    "\n",
    "We will allocate the period 1st September 2014 to 31st October to training set (2 months) and the period 1st November 2014 to 31st December 2014 to the test set (2 months). Since this is daily consumption of energy, there is a strong seasonal pattern, but the consumption is most similar to the consumption in the recent days. Therefore, using a relatively small window of time for training the data should be sufficient.\n",
    "\n",
    "> NOTE: Since function we use to fit ARIMA model uses in-sample validation during feeting, we will omit the validation data from this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_start_dt = '2014-11-01 00:00:00'\n",
    "test_start_dt = '2014-12-30 00:00:00'    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ElYaGx8mkZ2SEziFJploFbr01bIyElQJhjoum/xmg7/Omcfe1+IrD4WFg40bb\nlmZiwt5DAJ1zODAQ1gJgZqY+gQ8Vh4OD81McatuCaAvS5HEOQwWUfG6hjqOIw9BCQhqnUtumg2Gl\nrUG7OOIrDj/04w/hiq9f0TCOOYeEkK7nS18CrroqbEytZv+NjBRXkCZUHLrNnSkOSRIPPQRcdhmw\nbZv/GLc/X8ikemrKnvuheYraRRXNAo4Wn4I08j7OPNO6h9qwUtc5DBGH5bI9hlqXbD6KQzfnMPSe\nnCesVOMcasJKNcKrNDsrD31vUgAn5DxhWGln0Vae9hGH4g5Gs6sMURShWqtiaGCIziEhpLs5fLje\nLNsXuaFqCgTIJDd0IrJiRVhBGpn0jIwwrJQkIyLtm9/0H+OGlYZMKqam7DUTnN9SqU/gNc5hEeXu\nfQvSuOJQG1bq5hyGiMOpKZ04nJ6uhyeG5Dj2ijjUOIfac0vjwuYpSKNJLZDjoGk5o3EO84hDOof5\nyFOQZqAvvc/hjiM7sGRwCQ5MHgAA1KIaSqaEwb5BikNCSHdz9Gh4k3i5oWpKi2smuXnCSukckjQk\nZy3kHImHlbZbHIrwmg9hpQMDwCmnANu368NKtTmHk5N6cTg4GC4yekkcBofVKc+t6WnrwoY6h7Ko\nWESxF6041LTO0Lqb8lkVcW3PZ9rpHP7myG9w3uh5KM+UMVmZRKVWwUBpAAv6FrAgDSGkuzl6NLxJ\ntts4WbOSW0RYqZtzSOeQJCHOU8g54vbnC1lx1ooT2d58CStduxbYtUsfVirhuaE5h+Ichh7Hclnn\nQMk2ilqc+va3gccfDxujzTnUFCQDdPfyvJEqRYlDTeuMvGGlFIf50Fbr9RGHTx55EhuWbMDq4dUY\nOzZ2fMxA3wCdQ0JId6MRh1ongwVpSLeh6U3mOodFhJW6Tn23ikM5Jj7icN06Kw61YaUiKjU5h4OD\nusWpBQvCF5qmpuy40Purlhe/GHjb28LG5HEONeeW3Ms1izGanMOiw0qXLNGHlWrOrSJCxuczblhp\npeKfC+4jDo+Uj2Dp4FKMjoxibKIuDhf0LejegjTGmDOMMZPGmBucn11mjHnIGHPUGHOLMWZjbMyH\njTH7jDFjxpgPxX63yRhzqzFmwhizxRhzWVHvhRCiRxtWqnlYawvSlMsMKyXtQcRhyMRMW5DGdQ5D\nW1LI9VZE6J+GWq045/DoUStMQsWhG9ZeVFjpqlXFiUMg/F4nIbqhzuHMjD0eoW1cNGGlMoHXtLLI\nE1Yaeix17117AAAgAElEQVQrlfDCRXmqlS5fTucwL27P2oEB/zD1yoztc1gyJdSiGmrRXFVZrpYx\n2D+I1SOrsXdiLyozFQz0DWCg1N3O4d8D+Ll8Y4xZBeBrAN4FYAWAuwB82fn9HwJ4OYCnAjgfwMuM\nMW9x/t4XZ8esAPBuAF81xqxs83sghOSk6LBSbUEabbVShpXW+eAHgVNP7fRedBcacRjvc+h7Ludx\nDvPkHBZVkKZUShZsUVSfiK1bB+zeXRd5gM45lDG+LQfyiENtWGnR4lB7L9eckwsXhlfe1TiH7uKI\nJp8v9HPT5vNpCtK4C6aaViJ0DvPh3sv7+wPynmddQGMM+kv9mKnNHVieKWOwbxCjI6PYO7G3wTns\nSnFojHktgIMAbnF+/AoAD0RRdGMURdMArgFwgTHmzNnfXwHgb6Io2hVF0S4A1wO4cvbvnQngIgDX\nRFFUjqLoRgD3AXhVEe+HEKInjzgMfVi7E5FQUbl0qX1o+04E6RzO5f77w3OS5jta51BbkEb6rrU6\nrPTHP557bczMFO8cJk2w5Ho0xgrCvj7gySfzOYfGhLmHsmCkiVzoFXGocbu0OYeacUU7h3lyDkND\nzY0JX4zU5uFTHLYGNwok5F4iQg9AYmjpVHUKg/2DGB0ebcg57MqCNMaYJQCuBfAOAMb51bkA7pVv\noig6BuDR2Z/P+f3s/+V35wDYGkXRRMLvCSFdytGj9gETEp6ldQ7z9NRauNCO9Z2IuK0sKA4tGzZ0\neg/8OHIkPFxNi5z3oRNBmVCEnJPasGq3ol7S9fac5wA//Wn9e3HqRkaKyzkslewkvtm9RCbqwuWX\nA3fcAaxebb/XOIdAuDjMG1Yaci8RcVjk/UcTVqoRzHJOasRhUTmH8gwYHg4Xh6WSzqUMdQC14xhW\n2hrc4mKh4nCgZG9oSeKwXC1jYf/C42Gl3V6Q5n0APhFF0c7YzxcBiBdcPgJgccLvj8z+zGcsIaRL\nkVXt0B6C2jA3cUA0YV0h22O10rksWWK/7t/f2f3IYuVK4E1vKmZblYo9Ltqw0pBQJK1zJedyVnXO\nm26q/9+t8Fikc5gU6invXfjbvwW++lXgmc+03xtj/6WFKI6P27/rFrIJmtBV9QWxQu8/QO+ElWpy\nDl1RqRGH2mqloeJQHMdQN6+o8FBZsAitlknnsDXE7+WtdA7dsNKxY2ONrSw8CtL0Z76iRRhjLgTw\nXwBc2OTXRwEsif1sKYDxhN8vnf2Zz9g5XHPNNcf/f8kll+CSSy5J3XdCSHtwxeGyZX5jtCu5Pg5I\nM9zJ2eSkfXD77iPDSuvI5GP7divAupVq1bpKRSC9ybRhpaH9+bTiUIRXs8mLCKpHHpk7JtTd1yLO\nYalUF3lyjIC5zuGGDXOdbDmWpYQl8yVLgC99qbE/YkivQ61zKGGlURQ+gV+5srvDSvOck5oWAFLR\nU7uoGDpOIyo1LSnEAQw9HtpoAhGH27b5jyFzcaNAku6vwhOHnsCmZZsAeIrD2YI08ZzDH9/+Y5S/\nX8Y11WtS960wcQjg+QA2AdhujDGwjl/JGHMOgH/CbA4hABhjRgCcBuCB2R89COACAL+Y/f7C2Z/J\n7041xow4oaUXAPiXpB1xxSEhpHOIKNQ4h5qwUs1ExC33TedQj0yQeiEU6eDBYrajdQ7dUKTQ5u21\nmi6EL2lbIo727p27jxrnUFw/Y9Jf5+KKQdnPNHHYDBGHzV736KP26/79jWGlIb0O8xakMSZ83IoV\nxYnDvj5/oSy4pfw1CxaasNLFi3Xnf+i5LOM0rSyWLdM5hxoHVqr8howrl21hp164l3czvs5huVrG\nKX97Co5cdQSLBxcHO4euOLzs0stQ+s8S3vPu96Cv1Idrr7226TaLDCv9OKzguxBWvP0TgG8B+F0A\nXwdwrjHmFcaYQQBXA7gniiJZi7wBwDuMMScZY9bD5ix+BgBmX3MPgKuNMYPGmFcCOA+2+ikhJyw7\ndhQ3ydUyMWHzfjQFAvKElWqrnIaKQzqHdeTYhUzMOkVRoa/Van7nsN0FUeS6SZq8iCDYs6dxjFYc\nXn+9/ReC6/g1OyYh4rAZP/uZ/fqb3zRWOS0q51DcndD7XejCG2CfG3fdFTYGqAvmELThoW4USOix\nXLRId/5rFiNlnMY5DBWHeY6jxnEsqhLxfMZ3oW/boW0AgINTdkJXrVXRV7IPgVRx2D+I1cONrSwA\neFUsLUwcRlE0FUXRXvkHGw46FUXRgSiK9sFWF/0ggAMALgbwWmfsxwF8A8D9sMVmboqi6BPOn38t\ngGfAVkH9AIBXRVHU5ZkthLSXk0+2jYm7mXLZOieafJM8YaWa0JuQ7bmtLCgOLTKR6PYJxZJ4kkIb\nEedQW5AmNKxUxInGcU8SonLtNhOHodcoYB3IsbGwMc2cQ5e84nD7dns/3bat+II02kJaWnH4hjcA\nF18cNgawC2GhaMVJnmqlixcX+7zROIeh4lAbZpsnrJfiMD++1UofO/gYAODg5EHM1GZgjEHJWPmW\nWq1Ucg4nbM6huI0+vQ6LDCttIIqia2Pf3wrg7JTXXwXgqoTfbQfwgpbuICHzgCee6PQeJCOFI0Kb\nGbthPppJbmjzak0Yq+scHjtm32dImNx8pFfE4fLltmJpEWhyDvMWpCmVWh9Wunq1PWZTU3YSncc5\nPHYsOe8vbR/b6Rxu3w4873nAAw/MdQ41OYeH4yX0UpBjWSrp+kyWy3PDbNPQ3qeyjm8ztGGN7jjf\nc7lWs+NCncO8zxuNczg6Gi5ENWG22uMvziHDSvPhG1b62AErDg9MHmgIKQWyq5WOLBiBMQaHpw4f\nH+fTzqLwPoeEkDDGxoAf/EA3tptv3uWyfShpJgaySlqphDknab3QkpBJdWhY6cCA/VcqFdcaoZuZ\nnLTHsdvDSot2DosKK83jQKUVpBHRMzpadw+1ecGAPU9C83SLcA5f8Qpg7Vr7txYurG9L4xwWkStd\nrerCUQcH/V/r4is+XVxR027nMI9zKwJKE1aqaWWhvSfkeZYyrLR4fJ1DN6zUWxzOhpUCwOjIKHaM\n7zje/sKnnQXFISFdzvvfD1x6qW5sNxdDkSp82omBMWEPerkRh0yoZXuhzYzdXAKGllp6pfy5THLT\n2hq0CncFPt5+IQltWGneVhZJkxfJiVuzpi4OZR+lbYzvewPsPSt0UasI5/DMM4Gbb7b5h+KuaQvS\nhBRu0YpDbYP5BQv8X+sixz/kunFFje97i6L6YkCoONQ8b9znRq0Wfp1qWlmEFqTRimxtqoU4sN28\n+NwL+PY53DOxB32mDwcnDzb0OAQyqpX2WXG4ZmQNdhzZ0egcZrSzoDgkpMtZsUI/tpsdK2nurO1x\nBYSt5rq5UyHOoaavoruPoTkn85Fdu3pHHMq5WMTEp1q153BIM3ttWKnWOfQJK23mHPb1WRG1YEHY\nZ64Rh65z2Oz6zisOd+wATjrJvuYZz6j/XOMchnzWQL6+rpqJv9Y5lGOnSREIOY4ihEIXB/OEsMpi\nZGiOb1F9DvOElWoK2VQqVhxWq2HPUtKIey9PW7TeO7EXZ606K5dzuHN8Z1DOIcUhIV3O6Kj9GrL6\nDtRDn4pgfBx4y1vCxuQNKwXCJ8dFhpXSObT8+td2Yv3EE1YcdntYqexfEZ+Z5tzqVLXSrLBS1zl0\nz//QnCtNWKnrHDYTGnnE4dSU3Z9mvTk1OYcSDu9LnrBSzWKAJncQqL8nTeilRnQBYWIoj0umud7c\nYjshn3cecajN3dSElUoF726/n3cz8bDSpGtg7NgYzlp5VnDOoTiHoyOj2Hl0Z3dWKyWE6BCRF1ok\nQ1M9TsuePcC//VvYGBGH2rBSIPmGesMNwN13N/6sFWGlvhMfmYgDdA4fnO1I+9hjveUcFvGZaSZZ\neRZH8oaVNttWs7DSPOKwHc6huz9JJAmUPXvse2tWqEXjHGqLhoROxLWftzasVI5DqCsdKg7dCXXI\ns0N7PHwn8EnbGxgoThwWGVYq4xhaqse3IM3eib14yqqn4MDkAUzPTOtyDuNhpSxIQ0hvIw+WHTvC\nxg0NtX5fkiiXwx8SknOY1zlsdkP9H/8D+OM/bj4u1DnUlDKnc1jnwQfrRV56QRyWy3Y/ixSHIeHR\nRRekyQr983EOQz7zycl8OYetLkiza5ctRNMMTc6hNqxUW8gmVIzK5xYaLlit2s+63eIwnlYQEo6t\nbffgM4FPGhcqDqW9jaYgTZFhpXIv6ebUlW7HZy4TRRHGJsZwzupzMHZsDOPT41g8uPj479NaWSzs\nt87CnLBSFqQhpPeRB8uuXWHjigwrnZoKK6oBNOYc5nEOE5O49zR+nxUel0SeaqVAeK7QfOPee4GX\nvtT+v1fCSpcv717nsBUFabShf1nicO/e+hjZx9D+cMeO5atW2uqCNLt3A+vWNR+jdQ6LCivVjJPJ\n/vi4/xjZ3qJFYeeWG82hEYchYkibcxi/3kLDSkNbJ83MhBd70Yq8PNVKNQtNpBGfaqWHpg5heGAY\nG5duxJ6je3CkfARLB5ce/71vWOmuo7uOF7JhQRpC5gEykQh1n4oMKy2X7QQtZNLjhpVqwqyA9Id1\nXBxqJiKyvbSw0kOHgKuvBu66q3GM3PRDV473758/Sf4TE8B//AfwR39kv+8V51AjDv/0T8Mbjrtu\nXsjCQ56CNNrwxG4OK22nc7h7d7JzOB8L0shrNeJwZETnymkcOUBXrbq/3y5iasRoEc7hzEx4D8E8\nLUFCq8XKOM25RRrxcaUPTR3CsoXLsGZkDfZO7MXhqcNYMljvt+QbViqvBViQhpB5gbZ6otx0QlYt\ntcgDImQfWxVW2uwhv3DhXDGdVZI/a3tJDsjDDwPvex9w8cXAo48238eQ7b3+9cBtt/m/vpv5xS+A\ns8+uV3hcsqT7JxNSSj5UHP7d3wG//GXYmF4oSJOVqyuiZ8UK4MAB+7O8BWny5hy20jlMCysNLUij\nCfPU5oVpP285D0Nz3DXisBU5h6FhpTIutMopUEzOYbWaTxxOT4e328gTVtrt9/NuxseVnqxOYmhg\nCGsWrcGeiVnncGG4cwgAixfYcFQWpCFkHiAPltDJqqZ6nBbZRqg41ISV+oRirFkz92eaynhAfZKV\n9ZB/0YuAL3yhvi2tc3j4cPjErFsZH7dVHhcuBD7zGTvJ7mbnUM6l0Jyf+Hhfigwr1eYcZrk78h6W\nLwcOHmwcA+icw7zVSuPHxD1mSWjCSjU5h9qw0qIK0shrQz8DbVhpnpxDrcjTOI5A2EKfjAs5R2Tc\n0JAdE7Ktvj57DYS6m9qwUjqH+fFZRJ6sTGKofwhLB5diqjqFPRN7sGRBtnM4PTONBX22utSaETsh\numjdRQBYkIaQeYFWHMpKYOhk/Oc/D28ArnUOW9HKotkNVQqguCuoWf3a0rY3MJAeVvec5wBvelO9\nQmoecaiZHGvZvx/41rfa9/ePHrUTRgC48srw4iR5uPNO4Prrw8bIOTkyEvYZyHmmFYdFhJXmaWXh\nE1a6bJkNsQYaJ9QhrmgUtcc5rNXq4jGJbg8r1eSKaqqjyrkRsq1azf4bHtadW9pWFiHPDvec1PRH\nBMILQMmiSmhYqfRH9L0O3GgCjWDWtkkJPbfmM3v3Alu3ho3xuZdLYRljDEZHRvHI/kfmhJU2E3rV\nWvV464qVw7YPz0VrrThkQRrSUQ4dmj+5U50kr3MYOtH6rd8CvvGNsDEacdiKgjRJD2spO+/ujzsR\n8X3AR1G2cyKT43POqbdtcFtZhIaVavq8abn++nqxmHbgikOg2L5YH/0o8Bd/ETZGzsnQ9iNat0Xr\nHGonq3nCSkslez3EF47kPSxebN+/OB6asNJy2f6tmZlwtyXNOewGcehOqLu5IE25HF5EKM+5VaRz\nqKly6tvK4uc/Bx5/fO44Tc5hX5+9B2nFYWioLcNK8/OKVwCnnRY2xseVlrBSANi0dBPu2XNPQ1hp\nn+nDTG3uSen2Q+wv9ePq51+N89ecD4AFaXqCe+8Nr0JZNH/6p8BTnxo+bvly4LrrWr8/JxqVir3h\nh4q86Wl9pUwJD/MlT86htvw2kC7YgLqTIeNE5IXkt5RK6eE68vmccQbw5JP2GOR1DovqHdXuirbN\nxGFRzuHy5eFj5JwMFYfyWk2eVpZzeOxY4/mj7bumndBlrW7L3y2VbH+2Q4f04nBy0r4+ZGIMtNc5\n3LUrvVppSM5h3oI0RYnD0MJR2qI5eXMOQ+6t7nmsFZVJz4AjR+yi6pvfPHdcaFipjNM6hxrhy7DS\n/GgcVJ+CNG5LivNGz8OPt/+4wTnsK/VhJmq8eGqRXcErmfpN75pLrjnuJLIgTQ9w4YXAy1/e6b1I\n52c/Ax54QDf2V79q7b6ciFQqdtKlCStdskQnNEIr1eXJOWxHQRqZ5LsiN6uwBmDz4tx9kcm7bCvN\nORwYsA7Dzp2N40InB0U6h4OD7f37cXEYGhqXh2XLwsfIOVmUOMwqSLN/vw1xdcNjXVda6xxq2g0A\nza8BOf+BemipVhzKQotGZGQ5h82a2Ls0EyhRZCuwNsthBvQ5h0X2OdQUpNGIwzw9NLXOoaZADND6\nsNI77wTOPRe44476/vRKWGno8ZdxdA4bGRkJHxOScwhYcTgTzTS0smjmHLquYTOYc9gjbNvW6T1I\nRzPJErq5+ESvoBWH0kxX8xmEluTPk3PYjj6H09N2Qhd3Dt3wuGYV3d70JuD228O3JUJw+fK5k+PQ\nyYEm50qLOIch/SkBe5ze+tbs1zUTh0VNJpbOPj9DJj2uOAxpHSPXZuiiSlZY6e7d9ut//mf9Z+4x\n1RSk0TpQsr20SqBSlCYuDkPyKTWCpl3O4eSkHZfksGtyDrVhpaHh6W6xkdCcw2XLigkrzQrZTxsD\n6PoOAq0vSHP//cAll9hzff/+xnHasNI84jC092NoHj7DSueiEYehYaXP3fhcAPUcQqC5c+gjDukc\ndjl9fcC+fZ3ei3QoDjvL9LTeOVy6VCc0QnsqanMOW9HKotkNtVy24tB1DuVBaEz6pHrLlvr/fXIH\nxe0AmjsnIZMDKchRlHMoojD0HPnud4F/+qfs18XFYaiLmgf5fMfG/MfIOVmkc5gWVlqpWDf6Jz+p\n5/qNj9v8PqC4gjQ+YaVAc3EYkr+WJ3ctzTmMIp04dCMAmlFkQZqQz1oKxJRKurDSpUu7N6y0Fc6h\nRkClbe/++23qTbzXp/Q5DM2fzSMO0z7vBx5orCegzcOPt84gjc85X3wWOqaqU1jYZ1enLlh7Afb9\nxT689Mx6oQCNc8iw0h5g/fpO70E2ecQhV5XykzestAjn0DesNIpsKPUjj7TGOUy6oTZzDn16CgHA\nQw81bss3rBRoPjkOmRzIcSxKHMp2QnNMpW9hluMYF4eh7kce5FiG5HQXLQ7lnExy86angQ0bgFWr\n6osWrjgMDSvNE/qXtL14WGmz8z8kL6ybnMOs/oihfSbzOIeaEGJj9OKwWwvS+N7Hm+1ju1pZbN8O\nnHLKXHEooj7UOezvD7sH+bbb+NKXgP/zf+aOC3VutefWfKYVzmGza2CyUncOAesausKvz+icQxak\n6XJOOqnTe5CNTO5CbnBCtzuHb3oT8LrXdXov0hFxGFqgYWYmvJmu0K6w0u9/3xZh+v73G8Vh6Ep6\n1kpuknPos+L8xBPJY7LCSps5hyGTA5kMFBVWqhWHUqAjK4yyk+JQzskQ59CtFKgRh6FhpVn5ZHJu\nPec59dDSuDgsqpUF0PzzizuHcv7LNVqEOHSdw2bHRCsOs5zD0OOfJ+dQ664VWZCm6JxDrajUiCHZ\nXrNx1ar9XF1xqAkrrdXqLneIc+jzTASAX/7S/nNb74S60m40DcVhHRGHISkavgVpJOewGX0lhXPI\nVhbdz9Kl2a/pNHLCasJfu10cfv3rdjWtm9E4hxLqGNpXTm5soZNcX3H40EP2JvqjH7Wvz2EU1Z3D\nAwcax6W5C/JwlIc74BdW2sw5nJys5ymFTA7k+BXlHMr23OPkg2+blKNHG1dUOyEOJQfIB82qPZCv\nWmlfX3I4nlzHT3+6XVQB9GGlGudQWlekhWw2CyudmqoXOwo5/7UC1nUOm51jeZzD/uQ5VpA4EaGf\nJ6w01CUGwsWoJuJEG1Yq57+2z6E2rLTV25O/PTo6N6xUxviIBjc8VBtWmnae/PKX9rqSBVD3+Ic6\n4ADFYTNCFta9CtJUJ49XK22G2jlkQZruRh5Yof0ASyXgN79p/f40Qx7sboieL91+41i5Mvs1zdi8\nubFIRDuRwjIhk1WZiGj6VQHA4cNh+zg1Zc/JrG0dPmz76t1+uz2fliwJDyvNWgGuVu2+bNxoW0s0\nG9dsciATKCkCIn9LHoRJD9BmOYf799fPrZCw0k45h6HXtrwfH3HYSefQmLBFLZksjYyEi8PFi8MX\nw3zCSsWREAdUG1aqEV4y6ZRKn1lhpSIODx4EVqywP9O4NN3iHPqElfo+uw8dsscnT1hpEc5htRoe\ncaIV9XJ+yWcT76GZNCZvWGmrHUd5/82cQ8lx99nPdopDqbz7tKfV+zFm9TB139///b/2/+4zkeKw\njmae7FWQJhZWGkflHDLnsPuRG1ToBRZFNgm6CGTSrEk87nbnUCsOt20D/uM/wsZUq8CLXxy+rbzO\nYehDHgh3rnzzVA4ftg+nmRnghz+0eRp5nMOkyergoBXwblPirFU66Qs5NtZYjjzEOZScq337bJ6Y\njOt25zB0e3IcssZ1WhyuXx8mDvOElS5fHn4flwlrVljp6tX19xESVnr4MPD5z9e3FTqBd89/INk5\nlNfI4sjBg/U+k6FhpZq8yCx3M09Yaaucw7Exe0+Q4+EbfqYJ/csrDhctKi6sNFSwtSKsVOscpoWV\n9vfbBRERB5q8W3cfteIwsbDJlH1ObdwI7NjRuI9ZRdp+9Svb71p6rjKsdC7y+WY5h+554BtWmuYc\n9pf6Ua01DmS10nmAXIwap0Dj5GmQibvmJjBfxSEQXtFzfBz49rfDXWKNOBTBEhrm4xsuGKdctpPB\nrHFHjtj38rznAQ8+aMVh3lYW8eMpPQ6bicO0iUilYidFS5fWQxF9w0rjYXWuOCwqrNR1PH05dix8\nAQHoHXF40knhYaVFi8O0ib8s8qxa1dw5zDqeb3kL8IY3WCEiwmtgwG7LdyIu10zS9mq1+jUi5/+B\nA3XnsKicw04VpMlyu2o1GxI8NmZFvjE6N1VTkAYIDyOOInv+h0acSKSKRlQC/uLXZ0Kdta081VGT\nxOHAgL0uJbRck3frjhkaCitIkxX6KiH+69fbXrzN3lvSMXn0UXtebNnSeE10e7XSO+8Mb9OkxXee\nfN55wMteZv/v44JPVifTcw5ZkGZ+IieDRkQVLQ41N4FuvnEA9Qm85gYSKg7lsw7NS5qethPs0LyR\ngQG9cxgqFqRxso9zuHQp8FzbrgebNukLNADJDuDgoP3bTz5Zf+BlVQaTY7Z2bb3CpRtCkxZSJM7h\nU54C3HNPvrBSTRGhWs0WiQkVXseO2Ul86PZ8HeZOVys9+eRw51CTczg5ac//UHHoFuRIc6VXrbLv\nQ64TyedLm+R+7GP1svV79tQXOozRORmyvTTh1amw0k45h6VSusgol+3fvfBC+7wQUR/aSiGPcxgi\nzt02Cp1wDpudJzfeCHzgA/XruJ25g0n7mCWgZJ8WL7aLNxKiGSpi3eMREn7sE1Y6MWHvxSed1Ogc\n+vSMfOwx+/WBB3orrPSZzwR+/ONitiWfVdZ1s2sX8M1v2v/7LDxMVadaH1bKgjTdj2/uTjO6WRyK\n2PLJIegkUjQktDonoO8FGJrPJ45WyPEXwVKkc+gjDl3ncNky+6/VBWnEORwctBNUcdR8wlElv2vv\nXr9tueMA4KKL7M3/0KF6C5i0sNL77gP+/M/r309O2n0OdQ59Q1riTE5aEat1Drs953D9+jDnUCZL\noeJQE4rnbi/LlV650rpxP/hB47mUNsn9sz+zn88zn2knd/H82dAQvqTtucJLwqoPHNCHleYtSFO0\nc5h2HONuvuRuhhSlkWNSKtWrWWbhRjyELE7J+dgpcdjsWL7+9cC7322v5Qce8BNrafso29LkKqY5\nh/39Nod+fLyeA+t+3r6LMa449D1HfMSh6xyKOPQ9lo89Zl3vRx7pvbDSovqIy+ebdjykj7Hgs2DB\ngjQnKHIyFOEcTk3V809C0ISVymu7/cahnVRrxshx1IrDkGM5PW1vNqHO4fS0X2GZONWqfTD6OIdL\nlgDnnw/84hf2Z63ucyjOITBX6KXdiGUiuHKlPqy0VAJ++7fr/wfSJwY/+hHw0Y/W3WSpFBh63cjf\nD60ye+yYXhz296cLqFrN/n54uP6zosXhunVhlVi1YaXVqp14aZ3DrLDSgQF7D/iv/7Xx91mT3Msu\nA04/3U7u4udyK8NKXefw0KHOhJV2Y87hzp1WnL/0pY0/D3GFfPPCkvY7JOfZLZAUGlYqVXfz9NBs\n9t4GB4G77wauuMK2QHLP4xCRp8059An9k30S5zB+3mjEocbxBfycQzcyxudY7txpFz7HxnqvWmlR\nJoqPODx0yD4nJMfcpyDN9Mw0FvQtSPybLEgzT9E4h3JTCy0/f/fd9fyTEGSyHeLuSJuCbs85zBPW\nq3UOQ29WlYq9oYS6a5ocEE1PRdmeTzikhJUCwGmn2a/tcg6B5tXjgHTn0BWHoWGlAPDa1zb+Pm3l\nXly1n/ykvj2NAyXHL3TBIk9YaVYFXclnzBIX7ULyYENEnisOQ67vmRk7RptzmBVWCthwZwC45Zb6\n75MmuSKWvvc9G+r8wAP1e7KMa7a9LVvsQsWXvmSfE+6kM2mcK7wkx/P++xudwyLCSrvROdy50x6T\nU09t/LnGOZTt+RxL974VKs7TWqskIedJ6DM/HtaYFLJ5xhn2ebFzZ32BMW1M0rbaFY7qOodHjswN\nxw4JK5VxWucw6RyRKI7Vq+v5y76htvv3A2efbcf1UlgpUKw4LJXSj8f27cCGDfXwey9XulbFQCn5\nJjTV5hYAACAASURBVKR1DikOuxyZVGhC/0Ltcsl3cJt8+zA9bceGisNFi+qFELqVTojDIpxDuYFr\ncg59HMBm2/MRhxJW6qJxDtMeaK5zODraPEQ0qZWFm98VH5P08HQn8IBd4XZ7Jaat3Mf7DGodKK1z\nODXlV0goTrWavYgQDykFekMciuN+7FhYRUmtOHR7ocVxXWlZTHne8+q/Txo3Pm6PfakEXHwxcNdd\njW5e0rh3vhN4zWuA173OTsQnJ+c6v2mu3MgI8Pa329doqvXK9aZpb9MO5zBvn0MRh3/1V1Z4C5qc\nQ5/tNRsT6kDJ+a8Vh60OK5X3IuGQbiXcrPvJDTcAf/d39b+jaWXhk1ogz1vXOXQXVTQFafI4h2kF\naZKeb2kLDwcOAGedZZ+lPtE03USR4jBrnrZrl41mkarOExP1PsBp4jBN6GmcQ4aV9gAap0AeKtqC\nKKEtMDQFUWSVOnQFsmh8QgGSCD3+cvyKcg4lPCh04UE+65B8UTmPfcNKXdpRkEYm1OIchjTz1oSV\nuuLQGCtKhbSHfFwcymddlDjM6xSnCa9m4jAkDCwvvhV0XVyxNjAQ1g8wb1hplistiypZBWKAxpzX\npz/dVu1zJ9VJ48pl4Dvfsf9/7LH6hDJte3Hhde219pjLYmQv5xy6LknSmLT7pEwGh4et8yJoivTI\nOI04DM2vCw0rFXGiCSvNIw6zRN4ttwD//u/1bWnDSn1bWYg4jDuHadeAMbY5fXxb2gWErLDS5cvt\nIm2l4p9zKOIwHlZKcVjHRxwePWrPEcnNdgvXqcWhwjlkQZoewHdS7SI3Ga04dMv7+yDiMDTnsBfE\nYa84h0Ozxap8b8QiajTOoRRzCV05znIOq9V6NU6XVoeVuhNFcQ7loevTzDs0rNQVo81Ie4CKuDp4\nsL690GsNyCcOtU5xVlhpp51DcUU1YaVAWN6hiENtQRqfsNKNG+f+PmmS64Zvj47a88JtOZE0bnra\nFv946lNt+fpmBYV8XLmFTv2EIsJK2+kcpl3bWdVKxTn02VYS2rDSvDmHrQ4r/epX6wsP8e3JfsaP\niRvafNJJtiDK/v2NzmHacXzwQbswUqvpw0rdvOm0RZz+/nr0wNRUWDj9XXfZr+3MOZSFnlLJRhAc\nOOB3bkVRo3N45Eh9gdd3saITSNSHPFvbjYjDtOtGnMLly+2itUTYASkLD+1yDtnKoruZmSneOdTk\nM2mdw9DwlKLJIw41FT0BnXMoE6aQlURtzuGCBeGi0iesVNyM+CRNVkh9Q/iycgfdiaA4h/ECAUnh\nqAMDNuzGFYdZK7LxnMM4Wc7hsmWNYaVDQ3bfQh66RYtDn96bSeKwqMmEVNANCQ91J1kh14CElYac\nx7K9tII07sLD1VfbZtQuSeekKw6B+uKSOy5pe7/7u8Dv/751DsVtSNtelvDShJV2wjlsJvTi9404\nvmGloeOS9kHrHIbmHIbe/33CSt/9buBFL2rc/3ioZ/xzk/dhjF0c2bkT+OQnsx1wwH7mDz1kx2/f\n7pff1Yzx8Wwx5O7n4sX2WRdSkEZyAIvIOQTqfVN9cg4nJuxr1q2z/9+9W9empmjkvXSTcyjicNky\nYOtWexzdBeum51Y7nEMWpOl+inQOtZVRtTmHveAc+vamaUboJFeOX2ifQxF6IXk48rDSVCvV5Cr6\niEO3vL1LqRReWjxtsuROBEdH6+Iwq7CG23Bc8hR9KuPFw0rjZInD9esbncOBgfDJcaecw9CcQ/lc\nimhxUy5bwRZS/MOdnIWu3A8M2HPEdwxQP5fTqiDKubVwIXDmmY2/T5pQuGGlQGPeoIxLO5c3bgR+\n8xtdWGmcXq5Wmrcgza5dxTmHN95oKx/Hx4Q4t61wDpM+t3POsV8ffHDuOCC7WuzwMPCv/2r/7xNW\num2bnXyffroVNBrnsFKx14QsrmTlHAL2OXjgQJg4dHMA21mtVK7l1avtNn3OrQMHbDRNqWR7B995\nZ2+IwzwL/xoqFfvZ+4jD5cvt4puElAI5xCFzDucnGuewUrE379BeaHmdw/kaVhpaEMgdG4IcP03o\nmRRp8J3kiqjROIcacSjncdoYN1ckTivzK+LO4d69cx2AtII0mzfbVb0o8suvyBNWKuLQdQ41k2Pf\naqXVar3HlYwrMucQKG5CIZ9nSHhonsmZ5nNzcxx9ih3FSRoXdw7j4jBre9JXsZlzGCq8erlaaSuc\nw3Xr5v48xEH3cQ6jCHjrW23P1Pvua7wnhTq34hyGPDfk+Kc9byoVe17dfnv9ZyHiEKgvjvgUpNmy\nxQrStWvtAqFvzuHnP28Xvr7yFbvYtnhxejpCfD/Xr7epO/GCNM3GySKZW1HbN+fwscfq9SN8CtJM\nTNTvA6OjVjD75By6hawuusjmcfqKwwMHwmtctAptz+Y82/N1Dk8+2eaZynEEinUOWa20B9A4h9PT\ndlW4F8JKe0EcLl5cjHOYRxyGVoKTlUxNzmE7nUP3ZuiicUUBP3G4Z4895m4eVLNx8t4l1GP//rkh\nRa0OKz12rFEcuqK+Hc7h1VfbB5M7ThtWqnEOgeLEoeueh+QOuuIwRNRoWwCkFaTJWnhImqzKpFaI\nVwjOcg4lJynuHHZrWGk3OodTU/ZzcN0Bd5wmUiJp3H332c/7z/4MuPnmxrwwzSKHpiBNqZT+uVWr\nwP/8n8CHP1x/TZabF68We/rp9qucz2nH/8EHgXPPbUwt8KlW+r//tw2Bfd/7Go8jkB1WCtjWMQ8+\nODfnsNlnIMdY+g6G5By+//22X7AUwMlsiVCtP6fOOMOGqPucW24Ruac9DXj4YX9xeO21dh87QSec\nQ19xeMYZwM9+1jnnkAVpegARh6HujojDkPyWPOIwNKxUJsxFicNjx4Af/CB8nOb4yzEPneDK8SvC\nOZQxRTmHPuIwzTkMzaf0zTmUnk6HDjVOkNOcQ2PsJOTRR+0+S3iedgKfJjImJ23YWbOwUk17myxx\n6PbIk3HasNKs6pxx10koShzKRHd42P/9xSdZoWFdWufQJ6y0GUkTivi4m24Cfv7z7HHNxKH7GbY7\nrNTNr+4G59CnlUVSiLRUKm223VbnHG7ZYqvSrl9vIyXc+50mPFGeG6G5ummfW6UCXHaZvR63bm0c\nB/hVix0etmJD3Ng0B/ZXv7JCbe1a/7DSatWGW15xhXV944ssze5d8nfkcz7rLHutuc+bpM9AFq2k\nSF3cOUz73CRK5LHH/MWhvP+zz7b5mD55mG47m4susl9FHGYtcqxZY7+GGhmtQParG51DccA76Ryy\nIE2Xoy1IMzxsb0Yhgi1PzmFoWKmIjKLE4Sc/CVx6afg4iRMPDaEBwo49YI/fkiXhx8MNWQsNK9VW\nK21HWGm7nMM0l2DBAvv5Pv5448M6rSANYFf2HnmkMXcr6eHpPpibkSYyWhVW6ps7++ijjfsN2IdV\nO8RhtziHoVVHtYU8tI5XVkEaTVhpPBxv82bgGc9oHOfjHPZKWKnrHDabZLWzlUWSOBFxGDrOpVbz\nKzayfbvNE1292orDw4fr9y3N8ZdQZ62obIbMCU49NUwcxsX5vffWnaw0cbJ3rxWG4hz6hJWOjdlz\nf3TUuob79zc6h2lFc4SzzgL+8z/tZyEkXW9xcRjPFU173o+N2c/4kUf8zhH3b59zDvDAA/azciux\nNjsmk5P1nEsRh77VYuX8ue++5Ne0izz1JLTb861WKg74f/tv9d8V6hyyIE33owkrlZtsyKRHtgUU\nE1YqD1WNOHzta8OKOgDpD/A0NGGllYq9kMvlMOc2T36XOIeasNLQhQetczgyUu+d1Ix2OIc+YW6j\no/YBGncOm4WVykRcnEMfcZjHOXEL0kRRfnGYdQzlflGt6hcCZLxPT6dOi0PJnyoi5zDUOZRFprSC\nTNqwUm3zdlccHjxonZO8BWmKCCt1ncMkASt5Y0lonMO0VhZJlUqTttUMyRNLE76ALR60YYO9142N\nNeacakQeEBZa6uscuuLQbVMh+9nKnM99+2xxsXXrgCef9HNgd++2glLaPWzbNjestJk4dK/RTZvs\ne42Lw2bbO3bMPhM1zuHYGPDbv22fUz45h+77P/10G/q6YkV2WKnbymPNGns8fcNK5X3J1w0b6guh\n7aZatdd8NzqHw8P23Hrxi+u/S/zc2uUcsiBNd1Ot1kug+yIP8JGRMLu+6LBSTVgjAHz5y/XqXb7E\nCy74ogkrrVbtQzDkoQvYm8bSpfqwUo2ACs0dyRNWKvuY9P6ynMOQ95YWChOfrJ55JvCjH2Xnjrgu\nTYhz6E5Mm5G2cn/sWD3HcXIyXyEhIP3BND5utyOtOuSzHhwMX4ypVLrfOcwbVtrugjTx89gnPDRO\nWnhcVpGkNHEo94Bdu/K3sgh1YDXVeuPOYavCSvM4h60Qh745b82cQ01YqSsgQhYW5boRQdMs1DYu\nDkWwp31uWeI87V4yNmaPx7nnWpcsnnPYbJyIQ8CO3bo1+/jHBazkdPs6h+vWNReHWc7h3r3Ab/2W\njYoJDStdutTO6eS9Jr03oNE5BIDPfAa4+OL0bQnyvqRC+5NP2s+iCDRzuzyEVCsF7CKCS6Lj6+Mc\nhrayYM5h9zMzEy4O5SYbKg5lW6EhlLWavTmE5tLkCSsNbQKuFYc+oQBxtHlhGnHorq6GOIduWGlR\nOYfibiZNRLKqlWrCSn0mgpdfbhswZzmHrkvjOodZlfHyOCfy4F2+3B4f7eRY7h9p9xEJc5MKrvJ+\nQ4S5IIta3SoOXVcuT0Ga0CqPIfe7eKXAtDDPJNLEYZbjkrW9FSusIxUvSNPqsNIjR4C//mv7mnY5\nh1HUvpzDtLDSJHGYFY4nxMVh0ucmzqGbY60JK83rHBqT/NnJZ3vKKdY1iYfjt1qci3N4xhlW9B08\nmB1W6orDVatsPl9WzmH8WhNR6I5Lc+VkwXRqyt85nJmxn/GZZ85tZp8mDt3jvWFDozhM2se4OHzh\nC7NbewiHD9tngNu+SyqzthsRa6HO4e7dwEtfqtueTzSNez91yZVzqGhlQXFYEAcP6kr2Vqv2QtMU\nexkZCQ8r1RS/WbAgzLWSbWmcK5nUhV7QUokytH+aJqzUfW+hE/jQsFKZeMlDN9Q5lImBb/GDPK0s\nssRhUp9DIOy9uWEuPk7G859vv2YVpHFdGqnmtm9fa8JKkx6g8uCVHK88YaX9/X7icHTUikP5rLXi\nsJvDSt2JkDbnMGQ/NWGlPk6GT1ipVhxm5Tied57taTY62jiu1WGlH/sY8Jd/aV2aVuUcdotzmJZz\n6HNuudWSgWRRKbmhIg61YaXueRO60CHnctJnJ/eb9ettOx1fcZjlHDY7HuWyveaXLbN/95xzbK5i\nljjcv9+KQsDu6xe+0OgA+ribcp6599S0sNKhIftZHT48N+c56b68f78ds2qVFYmhziEwVxym7WPS\n4ruPONywwYpD+dtuK6V24s7tQtJ/HngA+Na3wrcXElbajFw5hyxI07388R/rSvZqnEOZMAwPh4eV\nhhaWkclCqDjUFqSRiyO0upWIwqw+b3G0YaWa0D9NQRr3hq7JOTQmzD3M6xymPdAOHmxNQRq3SIaP\nkyGVwbIcEHcivmpVveqciEOZGMQfND6T4yxxuHx5fnGYdW2LOIyHlWrEYbeHlbqTrJBzOU/OoSyO\n+B7LuBBNCivNEifNFsR8JtXxz0D6eoo4/PCHgbe/3ToF7jiNc+jTADyPOPTJOWxXK4ukBclWhZX6\nOFDufXt0FPjFL/JVKwV0YaVA9rnsikP3HNWIw6TjsX+/bRMgeabr19uQxrTiN7KPcv6/4AXAq18N\nvOtd9d/75BwKrtuWFlY6PFwXh+79J22h9ehRO5eQiBOfgjTx4611Dl2yFjlEHI6P16/nJ55Ifn0r\nkfSfvr6wtAm5bnwX1AWfwo3HjnWHc+hTkCblsiMhhObsAPWQTU1PMykkEeocjoyEFyhZsCB8AqkV\nUHKj0fZwjK+0ZiGT6v37w8ZoJjCasFL34agt2iLubdJNqdm4BQvCxaGbc9KMrLBS3/fmhmb4OIfi\nKruJ8ElV59yH/NvfDvzBH9R7EZVK9l88xzCPOIwXAMmTczgykp2jsmaNfe2BA/X7SFZuSzPkXpI2\nrlucw5DrJm9YqabdDJA8ycrKZ00qiKIJh5TJu0yozz3Xhnu6tCPn8MgRe9xEHC5c2D3OYZ6w0iJz\nDl0R+7znWcfLDSvVOoe+zwD3+GYJ2LVrrbs5NZXtHGqPv4hDQRxVETlp4lC254rCtO01E7APPWTD\nZ91xPuLQN+dQnhvNxKFPQRoAeN3r5vb+Tco5THpuZ4VHHz5sTZMjR+rXs/R0bDfxZ2laeL6LLP7u\n398YNZFGrWbHZaVaxPstuxTuHLIgTTFoqmW6oUga5zC0kMTMjK5thohDTZibVhxqHECgMbbdd1xR\nzqGElRbhHLoPudAVYMlV1IaVJp3LaQVpQq4BV3SETATdBQcfd+ENb7CrzfHV1VBXIk0MyYTJdQ41\nOYc+zqFMNCWEVbYtxyIkJFvEYS84hyHiN09BmqzFkTiugE2aZGWdW9qw0qyCTGnb04SVZuUcXnBB\ndzqHWWGladVK03IOteIwS3gB9r718pfbUEogX85hK51DOZYDAzZ6Yds2+zkLmuOflqvrTsIlVFQ+\nj1aGYzcb85Sn+AmvNHGYdi+Ra3XZMn1Y6aWXAs9+duN7SxOwzQgJK5VzKXQhUoucO5ooKMAupvoi\nxz/rmmmHOOwv9aNaa/wQfAvSRCnxtnQOW4RWHMqDUBOyqXHzFi0KSwjWhpXKhRmac6h1DuWiymoC\n3mx72oI0mrDSdet0jhyQ/hl885u2cpnkR7gP1cHB8LC6oaF6pTHfcT5hpXkL0kirDJlU+Iq1e+5p\nnKglTXLjD/n16xu/l4ehO4l2XYtmpD1A5bgtXWofoHnCSrOcvPFxKwz7+mzSvSwyGVOfiLiTtSTk\neGcVqeq0c+guqoSIPHEXNI5LnrBSTT5rWlhpaAsMH3HYjrDSI0dsfqMUVemmnEOtczU5aZ9hSYth\nWeLwnnuAn/zEvg8f59A931/0IvvPHaNZsNCGlaYJWNnHjRuBX/6y8XmgDSv1EXnyXJT7edpxTJq8\na/dRxjU7JiIWRBwa47eo5YpDeW6EisM4ac5hUlhp1r1cqrFu2VI/lzRRdhryVv7WiMOs+1a5nPyM\nLTKstGRK6DN9c0Rlw2sSf0OC8LWsXTQTCiBfqGce51DTyiK0WmYrwkpD8JlUN9uWZgKjcQ7dCWSa\ngLruOuCmm+buIxC+cqxxDrPCSicnbdhF0kMmbQI5MwNceSVw1131fEMJffMVhxdcMLewgMZd0ITV\n+TiHixZZMZXn3PJ1DsWldN2G0HBIH3czSRz6FuPIQzx3sFVtUpKQyYF2W2lhpWnnVpJz1U3OoVzb\nSYvUR44Amzfny7ntlHOYNKGT/N6k3opZ4vD3fg9429usYA51DuPI8Ug6/nfcAXzlK/b/7vNGU63U\n3V7aPl58MfCd7zSKw6R8Po2giY9r5hy2SkBlLSDIuKTtDQyE5xyWy/b3fX12/nLwYPa9xEdoh+Yc\nZt0jy2X73O2Uc6idywBh4tD3vtUtYaVAdmgpxWGL0DiHmjwVIJ9zmCfnsIhWFp0IK80Kj2s2Rrsi\nFVo9K+6AJH3eO3falVh3W644DF05bnVYqZRW10yWvvMd4HOfA7773bmloJuNy3LyAP0EMkkcpuWF\nhYjDPKudixal3w+kuIWb36gVhz4Pwm4JKw11DosqSONe20WHleZxDkPFoVRbTgpbHh+34nD/fr04\n7DbnMC2kFEjP1YoiOzF9xSuA66+fW5AmtHCRuFFJ5/KrXw285jX2//GwRk04to+AffazbUXILOfQ\n5/j7CKHVq+vhm0nbajbOZ3tZCwhAtojV5hwC9hju3l3/Pk2IZj2n0kJfk8Yk3cujyO7nqlX2vXUq\nrFTrHIb02vZxDqMo3DmsRTXUohpKJvnmpXEOARtaWq4m32QZVtoi8oSVavL5NIItb85hNxekkYtK\n4ziGikN3Ah86buHC+oPax212H1ZJD7RazT4cXHHohj622zmUwkqlUvIDLe2mCKQ/ZG65xSaG79w5\nV3AkOYBpD0EZp5lAtss5XLzYVvDLG1bq4xwuXdpYkAYId7x89lHKtMcpKqxU4xy2oiBNqKAEWh9W\nqhE17QorBerHstl1eeSILd7Rq85hs+O/f39ySKmMS7oG9u6197h/+Ac7sX7lK+u/83HlmtEsHF7Y\nvr3+/3jEiWZRJSvnELCRHOWyX1hpK1q5jI7akNK0iBNAd93kCSuV7Q0PWwG1enVYziFgx9xzT3bI\nrHu/CdlHbbXS6Wm7vVWr7LVdtDh0o9dCFrrlmIcujmeJQ7n/Jc1Nmi5012bQX+qHSVpVR7JzONiX\nniMy2DdI57AI5METMunROofa5tUSVhpy0rvFb4poZZE3rDRkQiHjQluJuGF1oUI7NA8zHubW7Ka/\nb589F+69t/77uCvRTudQboxu7locn5Lwzd7bnj025Oktb7EFDNw2FknjfEWedgLZKnEoFc5Kpcaw\n0jwFabJyDpsVpAHC7iUyTu4JzVxwWTVuNhlNc01aha/jHideyEZTkEYbVqpxDnshrBRIP5YSVtqN\nzmGW25ImTtKOZVqkxBNP2Jy8deuAf/7neiseGRd/b1GUPfHPOpfFGcpbkClpH4FG4bVpk/0qFVVl\nXKjw8nXJnva0xt517S5I02w/0wSzxjmUxdZTT7V/Z8OG+rZaFTIL6MNKZR+lr265HN7TOw95o9c0\n6T9p9y3NArmPA6h1Dhf0LUhtZ0Fx2CLkhA8RNXkL0miKxMgDy3dyliesNI9zGBpWKu8nZFvyUC0y\nrDT0ZhV3DpvdiHfuBE4/3bYpeOSR+ra0k9zQ1bb4anOzczJrBTjpwfStbwHPepYNfXr8cX/nsB0i\nT7u9pAeo2zognnOocaWzcmclrHR01Ia9xcVhaPGVUskej2bjZCGk2XHp5rDSuOMYet2EupStcA6z\nXJqkcdqw0laFYwP2b0nRiqkp+//+fvuvVvN/TmU5V9pr2/27zdCK8zRx+MUvJoekNntvcs6mmAuJ\n57L8LfkcWuUcNhOw7v1G7uHue2lnQRpjgDPO8BsXuoiZN+fQDSuNH/9qtfnim3utnnqq/b/kVWrF\noTbnMOk8FjG0fLl9tknkSlEFaeT9FuEcyjWYNrcrlxXFjnzEYRPncKY2g75SevgUxaGC/fsbe6L5\nIBO5EFEjJ29RBWncMFbfCzRPQZpudw61q9R5Qv+ktHJoawkg+aa/e7dtt3DRRfXQ0mYPGR+0zmGW\nS6PNHZmasmEzp55qxeHhw9k5h+0SeYAurFS2FX/IxydL4+ONPTQ1OYc+YaWjo3YiePBgvpxDIPka\nSFsl7aWw0tDrJo9z2Gpx2A7nUHP+p+2n5BCXStbR3rPHbsOYsPure761euFH6xxqRM2RI8DHPga8\n5CXJ4+LvLSsqA0g+lycnrWu4YYMtfqN1Dt3j2+zcEmc3/hm4NQI0wqtUsvfVeGivTwhlkc5hVm5k\nM+cwLVc0Lg5PPjm7z6TWOUy7L6Tdy+WaLJWscJXiSkWHlWpzDlsdVjo1le4cqsVhE+dwJppBn6E4\nbDlnnAFcfnnYGK04LLogTejqdp5WFpoLU+MAyvZCx2nFeV5Rr3UOs8I+4uJQJgyhYaV5nENtWGnW\ng2lkxDqj99/furDSopxDY5InPq44bEW1Up+wUmNsWNcjj+QLKwW6Vxz6OodRBHz2s7bHnoxzHZB2\n9znMurZ9wkqjaO7Cg8YlaGdYadq9S0IaV660zcOlqnDINeD+nSIXjFotDh95xObj/dEfNR+X5Bz6\nFERJqiI9NGQXjMbGWuMchgjYrIW+rPeWdG/NE46qEYfagjQiYJqJw7Rx7rV6/vnAhRdmj9E6h2lC\n20ccAvbZvX27jVzplT6HmoJ8WWGldA57nIMHw8fICRHSSsF18kIngnmKxGidQ23RFo1zGJo7qBGV\nrkujeW8hx1HGaXIOs8JK5e9edBFw992N+wgUE1aa5dJowtyAxgfMOecAH/7w3BycIp3DVopR97Nd\nvDhfzmG1aifH5XJ6ywCpfLhpE/Doo/kK0gDdKw59Qp0BW8jhjW8Evv51+70251CzYNSKsFJjmlcC\n1Rak0UxytWHc8W2uW2fDnU8+2X7vew1Imwx3MazbxWHSBP7Xv268vzXbXjPhleVcJZ3LIg4lD7kV\nOYe+AvaJJ6xLKmTdI5NoZzhqnLSw3jTSxKibcxhfoEkaJ60sANvf+Gtf89tW2n6micqkcXJtNCvK\n5D4DRkft512kc5gn/aevL3ze6ho9zZ7BdA57HPlQzzorbJw8xDRxytqcQ4047OvTOYeabeUJK9UU\nlimVigsr1Rx/bc5hVkEa2R9xDqOos2GlSc6hNqRFbqhnnGGP21/+Zf33nShIkxQylbW9tNX0ZjmH\nodeN9L1q9llHUWOPpVNO0TuHvSIOfar+HTpkv+7ePXecZnKcdW+dmAA+/vG520oKPc5TSCX0emt3\nld8k50QmuZITJoU1fBck4zlRrQwrzbq2+/r01WKbXQO//nVjbpzPPvqGlfqIw3Y6h/HjsXFjdhSI\njzjUiMokcaj53LJCWJPGufsp4jCeU590r9SEemrd1Ky82zQBK8+AtWttQbnFi4vLOdRWK61W7X5q\nwkqzKrbTOexh9u+3X93Gsz7IQ6yI8ES3lYUm5zDkpt+KsFKNpa9xDkN7OLoiTyMqtZ9bSM5hPPQs\nyTns77cr7wsW2Nh+bVipGx6h2ce0nEOtcygPwb/5G/t9Vt8vrZPhI/I0eUmAvzjUutlZOQ9yTOT9\nnXSSXcnNU5AGSBeHmjyVVhF3AJOuU0kD2LWrPi5PQZqs47hlC/Cud83dR2PsZxMXGr7ncnycxqmv\n1dKLmgD5wrGzJrlr1tiv8uz1febExWGrncMicw7HxuxEOoms+0jauCRxODxsQ3rb6Rz67KO2Uu3R\nKAAAIABJREFU2EuRzmErFyOBuWGlExONobatFIc+eZitHOeKw1NOAR5+uHjnUNvnUCMOs56JdA57\nHDkhQsVJuRzmyAF6keG2stCKmlBxKO8trXn73r31m0TegjQa5zC0h6M7ES/ycwsRzO5NJyusFKi7\nh9oVYNdd1lZ41IaVNnt4xgtNxCevrXYOfZyTVoWVNitIo3Wz3Zy3pOPvPuDXrbN9FfPmHCaNcx3f\nOJ1wDpPe2/i4LZawc6f9vt0FafbtswuQ8cqEgN7xalYxU+McSluVNFopvIDGsFJpUC74XgPxJt2t\nLkjTrmqxrRrn464NDKTnq69Y0dhKRMa0O+fQpZX5fHnEYejxz7M4EncOQ8RhWjP1VjqH2nDUeNGc\nw4eLzzkU5zB0XrhkiS6sFEie8xbqHEZ0DluOfKihBVHE0dA0s/cRXvFxRRekKZWyHag1a4B3v7tx\nWxpx2NenE4falhTafMrQxYC8rSzSVh9dcXj33XNzDn0nuRp3ud1hpaH9wrqtlQWQHPokn9vwsD1G\nx47pcg5d5zBJHLoPeHEnZFKtDStN+ty6Iaw065wErHN41lmN4tB9b63ucyiRKY8/PvczaaXw8pkc\nt8oBzNPKwg0r/cM/BL7znfrvfK+BdjqHWdEErW5loblP+gqvZsf/2LHO5Rw220eN8G323rrROUwS\nXjInqNXsZ9Au51B7TLKcQ5+w0lNPtV/XretMtdKiwkqBLnEOa3QOW458qBrnMNQyl1VTiVX2nTDl\nCfWUCUyocwj4be/nP7df8ziHoSIPyBdWmtbMO2lcN4WVuq85+2ybt6INK3WdwxDXpJ1hpaE31HaL\nvFY6h25Y4YoV1n3XLKq44qTZtROfGIg4lNxqrTjUfG7dFlZ6xhl1cRh3wVvd51DE4datcyddeUI2\nW1GQpuhcXaDx+TIyArzwhfXf+Z6T7cw5LLpaqSYc2DesNMs5FHHYLuewHeGhaeM0blfRYaWyPWOs\ne7hzZ35x2I5zMm/O4Wmn2a9XXhk218pDHuewHWGldA57nLzOoUYsAGFCT+sc+k5gXNybUNr25GKX\n3J08zqFGHMo4TVipNPMOEUOa4++GlWoK0viElUo/wDzhQaHOoU9Yad5WFkl0YpLbKuckfkxWrrTi\nIW/OYbPjHxciIg7PPdd+DbknuPvdzc6hG1aa5hyefLJ1UCoVvXPiu/C2b5+dCG7darcdD4fUnJPa\nsNJWncd5JsdpFVJ9F6jcNhZJ2+qVnEONqPcVh82OpYhDyTnULo5k9TlsZ86hNqy0Ve5anrBS9/0t\nXWpD/fMWpGn1OeneE5vhIw5POgl47DFbhChpO63GnYOGCD0JK9UUlwT0zmHT6CKlc+gzjuIwEJm0\na5zDUHHoK7ySxuUJK9U4h2nhl7IivmeP/Rp3DkNcOa04zGoC3myM63j5js3jHObJOfQJKz31VDvx\n1IaVus6hpkBJ0uRY+7Bup3PY6bDSZuJQXpsn59DHOZTiH+ecY7/m6c/Xy86h9H5ctsxWLs0bVucT\nVnrWWfYa3bEDWL++/rs8zmFoGF8nrpussNI4WREPv/ylPdYSGpm1j1nFdrTOYbNqpXnyu9qRc5h0\n/OM5h+10DtuRO5i0PZ8iKq0K683rHLo5t+12DjULHa0IKwXqoaWh8yYtst+hzqGElWoid4AucQ4Z\nVtp6RN37nBiHD9dFjybn0F011RSE0LayCJkI+rqb27cDT32qnWxNT9dv6n199l/IxFP6tYWgDSvN\nCgVIG5enz2G7wkrXrLFJ7du22Qe+jGunc+gzEZ9PzmE7cg6BujgcGtKJw1LJP+dwwQLgrrvqDcfn\nW1ipz4IFUC8d34qwOp+ojH37gGc8o7k4zCO8mlUrLWqSm9c51FS1nZkBnvY025/SJ+fQp9iOJudw\nvjiHbiuLduQcavMifStBa5xD7efWKsc9vr2lS+38yUccplWD1oq8PKIy5BkQWqtBi+y3xjns+ZxD\nhpW2nnLZXqQ+E7Nly4B/+7f6uDxhpaFhXXmdw1aHle7cacOzVq+25bjdiW+oC1VUWKm7j6FhvRrn\nUI6lNucwbUIh78MY4PTTbdnozZvtzzThcXKjSwr/iCLbRD2+j72Uc9gqlyZrAglk5xy6nHRSeDi2\nTKKSnMNmE4P/n703j7LsuuqDf/dVz1XV1XO3JktqWfIUS5ad2HiIEVbifDYBrJjExAEWa7ECX8IX\nPCzggzgsi3zMC1bisLwSEoghQEyIcXCY4izHKBCMbWQhD5LakiW1ZrXUU3XX1EPV/f44vX3Pu3WH\nvX9neO+1aq/V63XVe6fuefeeYf/277f3efWrq/9bweEkyUr7cg5nZoDdu9c7x31BlRMngL/8y+Hr\ndd3He+4B/ut/Bd72NuCTnwT+839ezxzmkpXGdHJDj7JgZKV/8RfudWFhvaw0tiqAYVsYkAdwoGYS\ncg61DGCstTwEHI6iWilQVesdt5zDGLJS3yaFObTKSvuIBo0PxIDDTYNNuLg2/AA2mMMEtrLiQF/f\nYJJI7dmz7jWkIA3Qr8N+6qn17axVNmWysAVpuq538qRjPg4edNLSEFkjW5DGKiv1+8jISnNVK/Wd\n1TZplr/oXHutez1wwL2yxWW6nIMvfMFVRr1wYbiPIbLSnMxh7Haa6Haf1ErydYsi/VEWdbOMf3/e\nMKDekt/Lmu/kdjkjCwvOEWir1ti1Jr/97cBrX1tdr0+V8ZnPAP/4HwN33OF+fuYZF1ATiy0rTcEc\nxgReAC8rffRR9/rEE+nPOcxdrbTPgbSeaQn0M4eSY7W0FA4O2+5/3xoZc01OyRzGZM7rOYeAPuew\na32NOSaZtUT62DS3rYor1kKYQ2vOoT/+2/w7SjK+drEX5G0abEpSkKYTkhZF8RsAerPFyrL87r7P\nTIoJOJQcujZ78kn36lc3Dck5bJuYZemize96F3DffS5HyN9AUjOHKys65vDECedgCTisyyGtzKE1\nsnTxoltcc8lKrbLeshwGh88/b+9jV/TRX3Rkc5EcG0ZWClTOQZNO/n/9L+dY3313PFlpTuYwpiMe\nK+fw4EG39gBpjrJg8kaarD63x5057HJGzp4dlpX6/e7r5+c/X80NX2retiY88ghw002ujTz7F72o\nej/EOWaqlTJ5eeMiK5W58cQT7pzKy6laaVe7JjCqrQTadC/ljMiicOz5Y4855QkQlznU3v/6/s2O\nrXEEh317t6QVbDCHcYxlDi9ccHuCVFXtWxPlWn37DaMmWC1Xx5Y5/BqAhy/9mwfwDgBTAJ681Pbb\nAJzu+RsTZSsrOlnpkSPu9fhx9xpyziHQPjGfftoBQ6Aq9hJ6lIVl0T91ym0aQPf1mphDX1aamjnM\nKSvVOIJNbaam3CbStVj9m38D/OAPVj/7wEsTfQSAb/u2aoMHwiqPtrX73Oec/PHP/1zHNjKSCqA7\nQtrWbhTMYYycw9/+beecAQ5AxC5I07fBd7Fk9b/VlwfbBw6112JNE7AA1p/zNj9fRfA1IFbAodz/\nrms9/HBVlEHugUi/264XIiu1zjdtXt44yErPn3fAOjVzGJJzyKx3zDoZIiv15+mePY6RlfEfmzlM\nuSZbZaVt9yMVONTISt/+dveqBYdtz7xtbveBPLaddQ8Y95zDixertK1UtSGaTMaQH+hbXVvFoOge\nXI3gMDVzWJblT8j/i6L4JIBvLsvyz7zfvQnAj3f2YMJMKysVmafk15Wli8DNz+uvpZGVinwGcBsh\nUG0GgwF3lIWF1hfQB/RXK335y11/HnkkjDmUA2H7FiXfRiErZYoIAd2y0g9/2J1T+LM/68aTZtGp\nOwfvelcVUADce9rvps05WVhwBYikEFHfOGY3667E+7br5TyvUApSaRiXvpxDP29KWxRLrI851BQj\n0AZw/PHWJSvtYoSsRwVZzR9vXeudOPFSrfHMGScpkn72MVeLi27c+DmfXcyhDwbrY60NHDJVNlPJ\nSmMzh6ys9Px5B7SfeqrKGxXLnXPIVitlwUl97oQUpPFB7J49Lugt438UzGGssaW5j217aU5Zqd/P\n224DvuM71h/LolEL9fVRghxda0nbdyuK7u/XNk+7ZKW5mMOpKRc4sspKhXGsB576rgXw4BConoHc\nbw3ImxpM0czhuYvtjqEl5/AbAHy29rvPAXi99g8URfEbRVE8UxTF6aIojhRF8b2Xfn9tURRrRVGc\nKYri7KXXD9Ta/lxRFMeLoni+KIqfrb13bVEUny6KYrEoivuLorjd8L2GTFuQ5vRpx6gdP15FSKxM\nnj952haPRx6p/u+Dwy1buJy3PlnpmTNuQvzWb7nPiFwU6JeV7t0LvOENFZvEMoebN9tldRcvukWV\nlZVagV4oOGxarC5edM7O614H/MEfrO+jVlZaNys41zCH5845dvLJJ6u8LbkWGxG35lZIu1iR3D6W\noOl6mmsBOlmpbwJotEfAaJhDxlltMo2stOu55ZaVdq138gyuuMKpNLTgcH6+kjOeOlU5Xl3XOnXK\ntWmz3DlXjAMfIse2yuP6mMOrr3bB2SeeGM7dzKkKCJHwMWs586yB7sCitN2zx41RBhz6IJodIyGM\no5U5ZBnf2OO/nr/90Y8OAzgGaLBjpA1UMrmiQPv+lgscyne2jGOg6rcFVMYGh1//uwqQt2mwaeTV\nSv8KwE8XRbEdAC69/hSAew1/42cAXF+W5S4A3wrgJ4uiuPXSeyWAubIsZ8uy3FmW5U9Jo6Iovv/S\n518J4GYA31IUxfd5f/ejAL4AYA+AfwHgY0VR7DX06+smstI+kDE/D9x4o9ucWHBYl5U2DahHHgF+\n/McdoyTgUBjHtknZdT3JlWubLP/gHziQ9Z3fCXz5yxXoA7oBmzCMb3yjkxyurPDM4aZNXM7V9u3d\n1/nMZxwj9+d/Xl1LWxCoqY9WcCjPuo05XFpyr9/3fcDv/m51rS65jvztrkUn5FiKLnB4ww3rwWHX\nJsgcZTFuzGEucFgUNjYvNOfQci1NwGLUOYf+OO6ap/IMrrnGSXqXlioWqqufp087hcmBA66wTN8c\nBXQAnXU8cx1lEZMVArrlcX3M7ZVXun3n6NHh3M1xkZUy4ETDXDXllzLHRNT7KWefsrJSuU8xxxYT\nsAP6ZfSjyDmMBUb7+tk2jllwyLQDusFhjoI0vtTfst/IM7HkKtbTf5jAD9DgX5RrnKw0c7XS7wHw\nRgDzRVEcg8tBfBMAdTGasizvL8tSbncBBwhv8H5u6893A/jFsiyfKcvyGQC/cKk/KIriJgC3Ariz\nLMtzZVl+HMCXALxT/9UqW1lxju7Fi81yEbHTp51z7DOH1uqhGlnpU0+5qOjVV1dFcARosOCwK7q9\nvAx84APAN36jyx08eXL4vLy26wnDODfnXh9+eDgCaXFyGXB48WI/OPzAB4Af+zHg13/d/exvxBaH\nVSa5BRz6z7pt0xW25VWvAh54oLqWVVZaNytzq2EOV1YqcChFPaSPMSPpuY+ysEZJU4FDgJN15co5\n7DvKgi00Esvq47jtej44vP9+N459J7cLHO7e7Y7uefTRSgrWtSYz+WRsziFbkCaXHNvvJysrnZ52\ne/UXv1hVaAbyykpjVyvtcyDbnjWbc+j384or3Ou4yUr71mRWVpobHDJs0qiZw759o60dMHrmUHxJ\nCzkBVGunJVexHsRnmcP6WNYwgAIe18oKsGRlDsuyPFqW5RsAvBiOxXtxWZZvKMvyqPZvAEBRFB8u\nimIRwAMAngbwR3IJAEeLoni8KIr/WGP+XgHgi97PX7z0OwB4OYBHyrJcbHnfZCsrukOo5+dd/sip\nU8PgMIQ5bGNOtm1zTogUvxGgYXWyfHDYNlm+9jVXbv3664Ef+iEHDqUgTRe74BdyOHzYMR/XXON+\nzsEcCjjsO4D6gx8Enn12+FrSRytzaNk8/YWyiyXbutWBrkceqZLB+1gaDSuXgjm87jp3T0+dSscc\njvtRFprINtA8V/s2CxYc5sg5DImS5gKHGuWCfO6aa5ysVBzjvn6eOuWYw337HOPYFxwBuIq9IYe3\nW5mT3FV+gTBZ6ZYt7v6fOJGOOcxdrZTJedMwEpp5KuDwcihIo1nvcoJDNsdx1OAwhDlsG5e5C9JY\nmcMYslLmOC9g/TjRMIDAevZwJOcclmX5OIDPA3iyKIpBUfRwnuvb/wCAGTjW8eMAzgE4DuBvALgW\nwGsAzAL4La/ZDBxTKXbm0u+a3pP3Zy39EltZcWCsD5ycPu2c49OneXCoOcpC2KR9+6rjD2TwxpaV\nLi46MHjNNcChQxV7pZFM+dLCw4ddPposmNZqmaysdOvWqjhEkz3+OPA3/6Z79a8F2JlDRlaqKeKx\ndavbnLdvd8zt4mKVEM2ycimqlZ475yL3UuGuzzlmcw5THmURS1baF1mV6+VgDkOOsmByDrvkam33\nJbestOt68gzEIfZzPDWy0j173Hoi478LZDPgkJGVlmX/uMwtK+0K/jBBBNkX5cgpv4iHzFH/WeYG\nhwzIA/qBXlO7kKMsmphD2cf71h///qZkDnMeZTHKgjRt7SaZOWy65rgzh3KvrODQD6qzzGGTrLSP\nAQSwriiNmjlciwAOi6K4siiK/1YUxQkAFwFc8P6ZrHT2GQDXAPgnZVkulmV5T1mWa2VZPg/g/wHw\n1qIopKjvAgAvpou5S79rek/eP2vtF1AxdX0Hlc/POynL/Lz7HFuQxmeTmgaUAMj9+ytwKL+zOHRA\nv6z06afdBjEYVOet+da1WS8uViWYDx9253lp2tUthDkUwNa0EMzPu3t1yy3VcQH+RswwJ1ZZaV/x\nIT+/7oYbnDT3xImqiEUqWen991cbvZY5lDF/4IDrZx9zyG7WXc5jWzttnkoMB2aSZKWxmEONrLTr\neinB4X/8j47N9p0aAU9NQSN5BlIEQqpQ9/WzCxyyOYexZKVyNldfZcKcstIu5oSRlcpaefLkcDEa\noKqu6D/vVDmHg4G73/XCUTnBiYbd0YAM2fPlefStP4MB8Ku/WvVhnJjDFKArpI+MwoLtZ0yQ16dA\n6OrjqHMOWebQB4eWoyxiyErXKZPK/qMsAKwrShODOezp6pD9MoAlALcD+N8A3gzgTlSyUMY2oco5\nrFuJCrzeB+AWAHdf+vlVl34n7x0uimLak5beAuA32y565513fv3/t912G2677bav/2xhDvftcxHL\n48e5nEONrFRAhRTJOXeumnTnz3N0edchncLSnL50eqW/6bVt8Gtr1WG6gCtq8+Y3V+/nYA59qWdT\npcQnnnCM6N697v2zZ9fL46w5V33gcGUF+Ot/HfjJn3Qsc1/xIZ8lu/JKxxwePw7cemvVjmHlupys\npSXgFa9wxXpe/3obc7htmyti8MAD/c4xKytdW+M2ayZPhXGOxw0cdjGHsXIO6xX2msZyl8OaChyu\nrgLvf78bl/71i6K6Zh2I+N/l5S93gRJNP+fnnQR11y7gs5/tL2JTlpwjmErqPE7MYYis9FOfcmtr\nWz9lzKfKefOBqP+5EKc/RV6qJohz1VXD73WtP/dd8r7k7OVRMocMUxaTuY11zmGsdk15qdo90SrP\nlT5eTjmHcm8tOYf14Eg0cMjKSjuYw7vuugt33XUX7n/+fnz5uS+3/83eq1b2BgAvKstysSiKsizL\nL146iuIzAP5DX+OiKPYDeAuAPwCwDOBvA/gOAP+wKIrXAjgN4CG4iqMfAvAnZVkK+/efALy/KIo/\nhitc834A/xoAyrJ8qCiKewF8sCiKHwfwzQD+GoDfbeuLDw7rtrSkYw5Pn3aAbdcul8OWSlYqEdKi\nqKSlAirPneOZw7ajFGQy/7N/5s6v861tg5GDpGVy3HRTGHO4ZQsnK+1iDk+fdlH+oqhY2NSy0oce\ncpvoL/8y8KM/2n+4tg9q5VkfP97PHPZtaF2b/B9dCu088ogDh5acQ2EOP/WpdEdZMLKu1FUX/XYh\n4LBvs7CsJz5zuLjY/H5M5rAvqDIK5vArX3Gg7e673brsX1+ARh2I+HPnx34M+J3f0fXz/Hm3R+zd\n6wJPt9zift/ldE5NceeMWWWl2sJKTeNfc15nTOc4RFa6ZQtw++26fuYAJ3VwmIo5rDPgsQqivOQl\nLj1ErGv9F1DYBA43bVrvO02CrFQCOH1BtJjBkVQ5h8wYSSErHYecQ+t+I3slKyuNzRxqZKWWnEMh\nxH7/q7+Pf3/Pv8eRjx1p/JwlX3AVTk4KAKcvgb1FAFe1NxmyEsA/AfAEgJMAfh7Ae8qy/AMAhwH8\nD7hcwS8BWAHw7q83LMtfBvD7AL4MV2zmv5dl6QPS74DLWTwFd7zGO8uyPGH4bl+3xUUXBe47hFrO\nw9q92y2QLDjsq1bqA8idO4F7760W4i6H7r77gHe/e9hR7CtI4w/eK6907X1ru54vKW2yXMyhSAia\nnoG/UO3d6+RIrKxUCw4ffNCd+/iZz7jr+eCwLRDgg8Pjx4ePEumSb3QtOl0R+Icecq9HjlTfTcsc\nbt1alT/vy7nSyHzamEOrrCtnlHqSmEPm/jeZJqgyCnD4iU84Fvx//k83d/yD0duix/4z+M7vBP77\nf9f1U9buPXtcxd7QnNu26zGyUnb8a4rfxGI3xUKqlfblIluDOCES9VzMVRsrFIu58vfxvrQCoJJh\n+wGJEHY5RsAOCJOH5gpGSj9ZhrkNtMUcxxpZaddYbnoGuZlDyz4KVPfWAg5TyUrXyrXozKFYTFnp\n5wC8HcB/A/BJAP8FjgG8u6uRWFmWxwHc1vLebwP47Z72PwrgR1veexzAN2n60WcCdLZt6wYnIj/d\nvZtnDv2NUSO7ecMbnGRz69ZKKtXm0P3pn7oDVa+7Dvjpn66u1yWH1ESxmq63sDDsiDW1swKv2Myh\nf6/37HGgi5WVSru+CNiDD7pzHxcWHEMnOR0aWen+/S430mcOU8hKT550R2d89avuZ38Ba1tUV1fd\nv82bHXMIjI45FFmXH7m7HM45BMY751CcSCYYkAoc/tqvAR/7mKuy/JGPAD/3c9V7bQGSrmfQBw7l\n2B4gPDjSdr2UstJYjnhKWWmbc9Z3/mlM5rDPOW4CbCmZw1Sy0rr1BQf376+O1/LHXKy1VdsuFnPI\nsmSjOMqiSwnCjmMmyNHWDhi9rLTOHEoudp/Jve1TD9bbpJKVanIOp4qpkVYr/S64XEMAeC+APwHw\nFXgM36TaF74A/NVfuf8LOOxjDsWR92WlfYDmZ34GeMtbKke8zhy2yW5kE/zQh9xmKVXFuhy6xx8H\n/tE/An7zN6vfyWTt2iiYKFYfOBwH5tC/13v3OnAYKittWxTFHnwQuPFG4I47nOMq4NDCHMaSlbZ9\ntxMnHNviy4P6wKH0syiA177W/a4PLITkHKYAbLHyYsYNHKbKOZSAgPR7HGWlx4+7Ksk33+zWpL5j\nKcqy2xnv6qc8uyZwyDKHuWWlsRzxUcpKtddLrSaoy/hSMYc5wUnXvnHunCuaJsyhJuewzzGPycr1\nrXcxAVTqaqXWdjFzN0OYw1EXpJG+F0W/r+YbwxzWwWHIURbrZKVK5nC1rBrmPufwdFmWJy/9f7ks\ny/+vLMv/99Kh9BNtv/Ir7h/gnIo+5nBtrWJOtLLS1VXg53/enR/4S7/kfmc5ygJw7MzcXHWmU9eA\nf+wxl49x7Fg1UP0jMJjIRpesNDZzaC3u47frk4/FkJV2gWyxBx90uZdvfKNr05dz6EfD9+8HPv95\n58CKk9sFvJjoI+Duw3XXuQI98t36ZKU+iH3LW4BPftI55HItZhNkmUNpawVssaLbGpZGrmeVPsVk\nDkNyDhcXXbGpH/mR9f0eJ+bQD9gB68Fh/V72ycg04OTwYfezFOMaB1mpljmMBTJCneMucNglK+1j\nDnOCw0lgDmPO05UV58scO+Y+4+8JTc+aPa8z1f3PzW7G3rtzgkON4mFcmUMm+M/kHGquxahHVtfi\n5xyKRQOHRVFsLoriJ4qieLQoipWiKB659HPHEj0Z9vDDVc6Vhjn0i8RoZaVf+YrLz/qRH3G5MYCt\nWqnYNddUh8tLxLJeRhsAjh51rNWhQ5X0Q67XNZlZWemocw59fXlfzqFVVvqJTziJmpyp5TOHXQuO\ngEMB832yUj8QsH+/ywf8yEeqiCvreFrAoYY5FEm12Fvf2g8WNOCkyaEA7BHn3MxhX2QVyCsrZXIO\nuwJN997r+vLRj7rP+A49G9mODQ7LsmKhdu92v/PBYRMLEjJvpK1c6/hx98oy59LWv5ch418T7c8p\nK2WCCH3VSrtyDmOBQzZXiwEnUhClb56yxUYYcNglK52ZcXvpsWNu/+g6zii3rJQJhmkDMfLZ0D4C\nvFqr67k15eGHjGNmLZG2oyxI47N5Vh9UmENGVhr9nMMR5RxaZKU/D+BvAfh+uKMi/m+46qM/19Vo\nEuzhhyuppybn0I+SacHhkSOu+ueLX+zOE1xb0xWkqedWXH11BTb8fKu6PfOMKypz3XUOKAI65jCF\nrJRhDllZaRtz6INsq6z0He9w1UY///nhPsoi3HR+2qlTbmE5dKgC8+KsamSlt97qCtl8+7dX77Oy\n0j5weO21rsCS/K0+eUTXwfSsPJRlJIB4zCHDuEySrJRllx94wI3DzZvdWlIPqrSNyTanIgVzKPdy\nMNAzhyGMez1o9+ij7rVtTeibo03Xs4z/GM5q7nYAz1z15RzWHbSUOYdN7TQMYP27yfrTV4ArZ85h\n1/3fts35I089NewDjAMr17fesYXMmvoZwrhLXyztgO7v1+QXsuM4VFba1MfcBWkAjjkcxVEW65hD\nwzmHsZnDnq4O2d8HcItXBfSrRVHcA1c99H2GvzNWduGCY9YGA7fAaaqV+s7xrl3Ac8/15xyurFTH\nPUxPu2tpDkavOyGveIX7Jybt6oNO+njttc6hE4bRT9CtWypZKcscWhYQC3O4f391Npk8xy42Y9Mm\n4J3vdEdLyN/atGlYy15f4B9+2OVkFEUlOZN70HYtf1wNBu5oCd9SyUqvvdbGHHaBQ1YpuPk8AAAg\nAElEQVQeyjoGTW1DGBBrlHTcwGFXQRprDo7YAw8AL3uZW+fuuy+OrFSb/6E1f53MxRzK9f7wD53E\nDmhfE/rmaNP1tGOLkZVOAnMYKiv1206CrJRhFrR9TCEr3brVnY345JPjyRymKGQm1/ODPWtruudm\nBbD+teqmHV+W78bKSpmcw5wFaQCOObQQFDFlpUNruVJWOjWYGmm10jZxi6L+z/ja8887acTcHPC1\nr7kHs2WLjTkE+plDXwazc6c7j8v/O11SQ38T/IVfGH5f2tWddXFGDh50kid/oqaQlQoAarKuCGTd\nQpjDTZt0OYc33ugkn9dc039Pzp93G8mVV1bgsL4QSP6pb08/vf5QYYkUapjDJkslK736avc9L17U\nLah1WalvbRuFhjnMDQ5jOIIh4LBvs4gpK+3b5LvGyEMPAW96k1tHBBz2zZuu66VgDn0mScschjDu\nvurj7W8ffk/GiP+3GVlpyPhnov2pjxuw7jkhstIm5rBPnuuzSfJZDdBuq1ZqLSyTsmhR21juuv/y\nN5vYI585PHrU/W3ZE0LY5ViBBwvjZQWHsZQqTMBIzBp8SC0rtYJD/zzNVBbKHFr3X39PjMkcqgvS\nrFUPIXe10v8K4PeLovg7RVG8rCiK/wvA7136/cTawoKLeF17rYuQT0+7jUHLHGrBod9mbs7J+OSs\nRECfc1i3PsCwa5c7AN6fqKystIu5skRxuyxUVtrFHEofX/pSJyM+f75/8ZB8ygMHHHMi1+q7l888\nU1WVBVze1vd9X/e1NOCwjZXrWsC72p075xjtmRkX/fUXsDbnLIWslI02S9sYm3VqcNgka+yab5YI\nqzhDjHwM6GYOn3rKOYEveYmbNzGYw9jg0AcLbeDQyhzKd2vK6e47n49x/ENkpVZHsG389wGocZGV\nWgvSaAqihMjxmmTEVqY+JXPIzFOgfQ/wmcMjR9z+IWMnN3MYSw6cW1bKSM39tpb1PERWyjKHbd8v\nV86hPwcte47vS/b5rSI7TSUrXSvXOFlp5pzDHwHwKQAfBvAFAL8Ed5zFDxv+xtiZaOWvvRa4//6q\nsIqWORRH5ODBfnDoH2Y/Px8HHPYBvSZwyDqQXdFH9iiFtj7EPufQj/Tv3OlA/SOP9Du5koNaB4d1\n5rBukvMpdsst/SxxXx5NCCvXxaYWhQuQPP+8u34fc5hCVjoK5jCGAzNustKu9YAJIABVoOOaaxxQ\nHMejLOqy0s2bh8dom6y0a72T/K+mnOKuIxianoEWHLLOKnOURcj4F8As19UUzbEGJHPLSqWfMVQI\nfU51GzhkmEM2GKC5Zl8g8+qrHTgUSWlbm9yy0pRreaz9JlRWagk+hMij+9YSqxJtFDmH1r1006b+\nfeqpp5xi7nOfiysrHWIOR1ittLOrRVG8pfaruy79KwBIPPVNAD7d2YsxtrNnK3B4zz0VOLQyh69+\ndTXomw7b9CPbc3M6cCgV+BjA4DOHp07pmENWVqrZYBjmUPLgtO36mEO/jzfe6DY1kYS1fbc+cNjF\nHL7qVc197ZKwMsxhn3PQBs79e7Jzp5PC+rmjDDjcYA7br5caHA4G8ZnD1VUXNDh40AWZnnrKjZEQ\nCU0qWamMyYMHgX/6T4fXYIa59ftad5CszKGWFWKYQybnsKmPFnZNAKhl/FvHZV9BlNgFaYB8EnVW\nZtgUrNAEA1iHtW3v9mWlDzzg8vj9NjEZwFSBvpBgzKTJSkOCHEygCRj9OYepmcOPfcy9/tZvVYUe\n5Voh5xyyslIfHF5cu4hNg+6LheYc/mrL7wUYCkg83PN3xtZEVnr99e7YAHGOtcyhnF33qle5yScP\ntz4p6m1Onx4Gh20OxdQUV82qS1Ya4kAyoNLCHErUZuvWqjy8pZ0m5xBwFV//9E/7oz0+OJSD4v3v\n2+ZUP/MM8La3Nfe1izmU8dRksWWl/j2ZnXV99iPAbYtjl1P9QmMONY44wMmKLOBQxkDb/WdzDp9/\nvmLipPjE7t2VMzhOslIBC1u2AP/6Xw+/37QGWWRd9WBIKuYwFqBJxRz6/RRw2HettusB/eOkbfxr\n8qz9ttp5GitXiwmGsczhqGWlx49XBZnarjWuzGHOAEIs5lCKC1oKvLHjOERWOuqCNKHMYV+bxUWX\ni3/33cBrX5tOVqoqSFNkLkhTluX1Xe9fDiay0te8xlWY/Lt/1/1+61bHuDWZD/SuuAL49V+vQJ4M\n/CZwKI73zp3OEd+2rZuB6mMNpV3ThJb3YuYcdskHUjCHTEGaNuawfi9f9KLhKmNTU819FHB49dXV\neZEaCcHJk+7IjCbrk+q0WYisVAMOhRUSa1sc+6Rgk8Acxspv0TrHbcxhn2NmlZXGijSL+bmzu3a5\nzxw5Ukmmx0VWqsnXtR5lIe3a5k7b2tx0L1MXpKnLStnx33c/pK0AZnaOirHjRBNYGYrAK5zcpn6G\n5GpZqzNrxmNMAAXomMOuvUqKrtVlpZPAHOaWlQqoE0WDJTjlm4zlLil3rHGskZWOKzhMzRwuL7s8\n/P/xP4Cbb672n5BzDutzZ3VtFZunegYJHHO4WlYNNbLSOttYN0vO4WVpIiu98Ub382te4163bdPJ\nSgcD4Lu/u3qvbeDXZaVPPDHMEjVNMA04bJMryKS0MIfskQjWjdq3f/kvgV/8xeG/xYLDqSkbcwhU\nz6RPVnroUHV2of99rQtj17U04PDixfUFMmKAw127bOCwbZywzCHrGDS1TRltZqVIzFEKLDhscwIZ\ndvnEiSrIURTOGTx5sgKH48gcNhlz/4H2vnYxh31rsvZauWWljER0VLJS63jWOLkAt5Y0VSvtc8Zj\nsjQh1UpDZaWyXzz++HAfYzGHqZjbpnZsioCmnX/EjRjLHGrliTllpZa5nbIgzXvfW5E6ft9TMIfL\ny+64sueecyk5Bw6438dkDhlZaVmWKFH2FrIpigJbpto3zRc8OBTmcDAA/t2/A37gB9zvtbLSurWd\nM1bPU/zqV4er6bHgsE/CJODQd2ZYiUlIrmLbJPvJnwR+6IfW94EtSKPNOdy3z72+9a1VH7tkpYNB\nJaurM4dWcMje/8HAbTJNOSeh4HD3brfBa2WlXeAwFrs5rrJSxoFvYw5zFaTpczy7ghx1xh3oLlsP\ndDvjo2IOY4PDLuYwhoRsXGWlflumjW+srNQKaljmUAtOrGtyLECjbcc6rH2yUgB4y1uGjyaIFXhL\n3S4ncyjt/HvJKhcYcMiCbOZaYqNgDj/0IeCjH3X/1yi8mszCHM7MOOLgnnsSgUOiIM1quYpBMUDR\nVyUM2ACHXSY5hwDw/d9f5dJoC9LUre3wdj+B/o47gD/+4+GJ07TAxWIO6wVpuhw6RlYawhzecMP6\nv5WKOfTv5d/6W8DHP16BxDbnRcAh4Bzjxx8fnuSs09N0HzVROmYj1IDDPXvcd4shK72cmcNY0WZA\nN2804LAsK0AQWz5W3+CvvVbXbtyYwyagwcq6gP7gzyhlpTmYQys4jC0/7gN79T0nJXPI5hyy648V\niEo7ljnskpUCzpe5777ua4UE7FIFOnKDw/ocYNcfljlkfAvWJwHyF6SR+/R//o979fveFWiqm7Tr\n239XVtwxYC96kVNdHTxYXSsWOFwr18zMoUZSKrYBDjtMZKV1055ZqG3ny0pf/nL3+pWvVO83LXB9\nFdna2vmTcscOB4Duvz9OtdLYzKHc+9On3SsDDn3nuCvn0F+oZmcdSPf72CUrBdwicPSorpS/xnls\nkodqFmLrRmhhDjXgMBVzmBMc5nYo2pirPgdXs6GtrTlGWaRLrCpAM45/+IeBD3xguJ2VqRwnWSkT\nue+7XiyWkpWV5ihIY5WVMntOW1CxLHWy0hjMYc6cwxAJa8qcw6Y1aGWlUg9s2eKCi13XGteCNLnB\nYT1gMW6y0vq1tFJnSwAtFXMoPqRUlff7bimKKPdXwxxu3+7OzQbiMIf1eyksYJ9NDaaGmEMN2whs\ngMNOE1lp3frKaFvBYb3NBz+4vsJXCuawKIA3vxn49KfDys9LH61RdKD7Xj7/vHs9cWK4DxZwKJGe\notDnHNatT1YKuLzUhx4aDigw96TpsGUgXV6Gljl87jmdrLSPOWTAYajMjckTkqCCpV1scNg1JrWb\naD1CGjPnsN7Hl77UScH9djEZIdbYgjQhzOE4FaTJwXb5bXMxh03rjwRDLGuexjED4t7Lvvk2ybJS\n61m341iQJqaMmwmQjCM4bJKVhuQcNq15qXIOT550rz7RoGEO19aAP/kT9+pXgu3bpwQcCtkjZ563\nXSu1rHR1bbVqs8EchpsvK/WtazCxOYe+M3Hnna46qliI3rvP6fyGbwD+4i9GJyttu5dl6Y6HeNnL\nwsChvwhocw7r1gbylpbcAgAAN90E/NmfuZ+7jiDxv0ebsc5BG3MY6vjLeZ0xmENGVhoqc7NuhE0F\nApi8jHHIOayDwzYmr2uMWPNG/HaTICsddUEaRkKWWlbatI4o0lSiModd96UNmGjYDL+tBIGsjr+2\nHSsrzQmgWHCokZU2tckJfJmAaVO73LLSScg51MhK255b231JxRyeOuX8GAGHWubw3ntd3uxv/MYw\n0aBlDg8fdj/L2hkqKx16bkRBmg3mMJItL1fyCN+6HBgm57DpgHN/I445MetOz/XXO8ZrVAVp2ibm\nwoL7my96UThz2LcIaHKS+hiCm24C/vzPh/MkWTY1VrRT007LHALhzGGXZDYVcxgzAmydb5pNt6mP\nQD9g6NucFherPki/u8B5DOawqd2kMods5B7oZw5jSFhZ4MVKIfvOTmvqJzvXxJhxon1u8rx9h8/S\nTzl2oK8dowKJCYRYdq0s+32MtsCuteDaKGSl1jmQMhgp7erMIZtzyHw3Vh4d4oM2zZ0dO1zQPbad\nPOmAmpU5fPZZ9/rUU8PfVwsOv+VbgM9+tvp96FEWQ2u58pzDjZzDBNYE2oD0stK6sQtcm6zUH4TX\nXZf2KAuNA9k0yU6dclT83r3hzKH0mwVrGif3ppuqz/a1Y66XagOV95pky3XmUAoyAdxRFmxF1SZQ\nmRocMpt807X65mhTOyAs5/C55xzLu7Kik5Uyzqr0kZFjd42RtjkaYkzwJxVzmFtWui7arGQOJ1VW\nqnGO/f1bKykFhp8By9wC6XIOm67FAigNaO5ydC3M+TjKSmPJQy9nWSkLKrvWu507XWpObDt1yhEh\n8/NV4EPDHD7zjHudnx++txpwuG2b+/6ve131+9CgVl1Wqsk53FRsMIfRrc2p6Io0dDkiXeCwy3kJ\nWeD6GAmpMCgJs4zEB+AllG0TU6LvdXA4NdUuz22yeoQopjzObzc9DfzgDwJvf3t/O0auEyIHY9r5\n3+2GG4C///eB7/me6n1GVirXYgBsHVTmYA6t7WJFjQEeeAHAJz7hXo8e1TOHVlmvpo/jIivtC77F\nlpV2MYdtAbuUslKGOQxh6n0ApWXkrOMkRFbq798aMOn3U76bVhXAONUsEAopSMOy2dYAYYgqJiZz\nmEtWalGP+O1YcKgZ/zFlpUy7PnB45kz332Ts5Enn427d6lQ1FuZQ5KgW5lCqldata88wg0OlrHSo\nII2BOfzdf/C77X1R/YXL2NqAXlekoQtEtYGaNoZSLKakoj4xRSr4t/92exsgTFbKMIfSbt8+4Pjx\n4T6wstKuPjKy0vqz/tCH1reL5VRrmWJm45Xx7I9Bv487dwK/8zvr21hlpUD13fz7bdlk5B5YnNV6\nZcKUzKF/rdQ5h21r0D33uNeHH3bBlRjM4TjJSh980PX3xS/u/pwYc5RFSuYwV7S/qY+pC9L419M4\nqtImt6zU2kdgmJkLkcxOgqw0VFbd9dya+tgXRIgZIM8JDtkgTs6cw9z5rF0BmdlZBw5Fth3Lzp51\nf3turhnote05zz4LvOQljjmss40aWWndQtat+hjRsoC+rPTi2kVsGugWvMO7D7e+t8EcEsxh12aj\nOeewyWIu+k1Oz5/9GfAP/2F7G2lnZYSAfrDQxRxu3twuK9UmLWuYwy5nrqtd3z2J6VQzC3hZ6hZZ\nFpwwzGGsQEeIc6xhChjmsD6WR8UcLi05Jvvhh9fLSmMzh8z473IO2qTHvv3qrwIf+Uj7+3VjCtKw\nzvHaWvce0Da3rYGflLJSAUGMjNt/5lpWjmWYY8lKGebQso5Y1+Tc0ss2B74vOMLISnPnHMbab1im\nmJ2nmvsfExwy8tCQXMUuRcCWLQ5cae38+eb9qf6ZrVtdqlIdHHbtpT449NeKUYDDdXtAuaaTlQ42\nYbV0N+ji2kVsnuoZWArbAIctoKGPOWx7yGzOIUvpayVMb3pT1WcW5LF0eR9z6GvQQ5nDrj6y3y2F\n1DZEetPkQOYEh8w4YaRu2k2XBWzMJh+TOWSkx2LLy8ArX+kYthiy0txS8z728MwZt1lrLedRFl3F\nFoDme8lIxlMGR5qO02GcXH/sdVnT/tZXECWWrNTKHDI5h3Vw2FfIRtr44JyV8LEKI62zag0QNl1L\nU+wo5ndjAuspC5lJP/3rscGpnMxhiBy1657s3Glb39/zHuCjH+3+jAQId+1yElP/uJsusufkSZdW\nI4AyBXMoASNzwEIpEfWZwwtrF9TMYZdtgEOCOeyKRLKyUjbarGUO+9oA6WSlfczh9LSrXOr3IXdB\nGtY5bmpXT4ZuMnZDi+VAApc3c5hKVjpOzOHrXw988YvrI6SMrLSLOWQCAX0boQYcWvJSch5loVkT\nYkTgWblaSll1/XoWWWmb9KwNRIVE4GMUpGGZJE07AY/1HGuW3WGZQ1ZW2tV2FMxhLFkpE+iYBFlp\niJqAZQ672s3N2db3EyeAp5/u/owPDo8fH75HXWTPygpw6FBc5rDeTmTV1iC+RVZ6YdVd1CIr7bIN\ncNgBDtsGUx9l3tQuZUEaSzIwwEvIQhjHLuZweroqy8+Cwz7nONU9abr/8h36ZEWxNrTc4FDDCuWM\n5DaBQ6ZIxrjlHHYFqJaWgDe8wZ3RdOFCOuaQCarI+mgd/76lYA5jgsNxC/ywTm7o9Syy0qZ1khn/\nmmv69zK1rHQwsIO8+rWkHVOtVOv4t4HzLmOrleYEh7EkuimDik3tcjOHGjUBUxSOZQ4t4HB52VUj\n7TIfHB47NrwftO1vgNs7DhxYzxx27VFl6UCl9hg8ll1eK9dUzOGWqS24sFaBw82DDVlpsLUVKmFl\npX1AqM1iRgRTsWSsrLSPOZyZCQOH/oRu+24aJi+WHFUTERy19Ez6mUJWGiKZjQF8tVK3GMyhhaXx\n25VlWEGa5WXgqqtclbVHHhkGh/XoL5CWOWTlajmZw6Z1uW8ct/WTUVjkHv+pnVyfOWTmGtB/HydV\nVpqyXUi1Uis4l3ZW9Uju3MGYzCHDALLzNNX6I32MMSZTykot6/vSkg0cPvaYYyfF2vY3YBgcapnD\n1VUHpJu+Y9O6xfoJ2qMstkxtwflVl892YXVDVhrFYstK25yevskSc0FNcTaZXIsFlX3MochKZSOX\nfnQ5kP71+xKPQ3KurMyhlpHIKT3LKSsdBXNYr1aqcQZjMYdURHBtOCdC08a3pSUnaTlwwJ3TJM9R\nNizr/Y/JHMYChzGZwzZZKRu573rmucd/zHZWxt2Sc8gwsLFkpSxzOG7gkJUss0HMrmeQgjlkiiTF\n8p1yPO86OMzJHKbKp2xa78YRHHYxh3v2uNdz56p72wUOreOfXSe1slIfHG4UpIlkzFEWjKy0b5K1\nRV9iFaTpayPtGFlpKHPYJCsF9Oyh36atj7mZkz5wGMIuxGQO+/K0cjKH7IZWH1+s1C1nzmHIMQqA\nYw537HAb4RNPVMfVABwr3cY4jpI5tIDDnEdZpGQOc8tK/Xaaggn161lyDq3jRPpXH5NaWal/zqGW\nOfSZuZQ5h03tUksvm8ZxSLXSLvUU00e2SFIbOBk3cFjvZ6rgVFMfU49JKxiana2KEGpsackVjuky\nHxwePWoDh1u3OgXb/PywCgdoT6XqGv/M+t/UVluQZog53ChIE8dY5tAyMAAdOGF0yk0TM2fxFc31\nNMxhCDjUyEqZ+y/9sd4TdtFnNnlWriD9ZJ9bCuYwFvC1SN1CmUORl1ivFQoOhTncvduBw5mZ4XbW\n+9/kmGn6yUa2c8tKYxak6QNEuZmMUcpKLYEY6zgpCn58+c87RFbKBplSrT8xpZfaeVrfA+T6bddk\ngyPSNgY4YRivUTCH43SURVO7VLLSbdtczl6blaVLlxCz5BzOzQGPP86Bw1Onhu8tU3chhDlcNybL\nNY453Mg5DDeGOWRkpRpZV6xcgpDjF6xyKWkXUq10ZsbJSoW5kO9sYQ59WWlTHzXMSVsfrUAoRFaa\nqsJaTFnpuDGHTc6xVlYq12PPi7QwJww47CpIs2NHBQ77mENWHsTKShmZm2/CHNZZozZjCtKkcs5y\nB0ea5KEMYGOuZ5FLxWKYtTmHl6ustF6QJsRPYJkrq3+R8p6E7KXs+I+xT2lBHjP+c8pKGXDY59v9\n83/ujpcQs8pKn37aDg5nZ13eod9vJrWGJXqa2q6WGzmHIzOGOeyanG3tNOAkV0Swi13rA4csgOpj\nDpeW1vebYQ5DmFtGVtp2/xlZqVZGzLJrVlndpB5loXUG/evJtfrAYVNBGmbRD2XXfFnp44+HM4cA\np0Jok3SFfLdz56r8t67osm+srJR1jq3r6+VWkCaHrFTaNRUSsshKWeZQex8Hg2HANq4FaUIZWDHN\n+j8OzOG4y0rZPUDbbpRS51Bw+OlPu1cJDlrBIcCDw1TMYS5Z6UbOYSQ7f77Z+ekaTF0DYxJyDgcD\nN+msEUhWVtrHHE5NudeFBQ4c+s8jNvBloqRap3OU0kvpJ/PcNMwJu1nHAL4W5lCul/paDHPYtZac\nO+ekOU3MIXv/YzGHTJDDt7NnXcECv4pxn7EFaVLlHLI5UOz4ZyVkocxhiKxUs042PTcrqNF83u9n\nqKx0HHMO2SBmGzhPwZxLP2PIGlMrVWLISlOec9jkJ1DM1QhkpWtrwAMPuP/LZ5aXHXCr+6y+dYHD\nNr8Q4JjDrrkj9yuGwospSLORcxjBVlcdSGp6aLllpTmj/WxFwy5ZKcscSruZmfVRmz5w+I53AH/8\nxzpZaQjwtUZJNU4nK/1jNzTGOQ6RleZkDtmCNE3MYcprMbLSpjEp5ysVhQOH8/M6WWlO5jBEViqS\n2W3b9MfZpCxIY2WuWFlRrMBPTgaEzaUB0stK/YI0uWWlDKgcRUGaFLJS6aO16qjf1tIuxHeKAfJC\nmMNUOYcx1wQmHSGEOXz8cRcc3L3bgUIJhs7Odhcpkz1A5Kj+ftDlu8r7s7Prcw67SIOuZxDryCvL\nOYc+c7gBDgNNqjU2ScnaAA3QLytlwGEssABwsgNNH9u+WyhzCDhp6fy8HhyePw984hPAX/7l8PPo\nYg5TyErbnB5m0R9H5nBSjrJgpJ6xmMPc4FCK0QBVlDSGrJRhwWONY9+Wl9336yta4FtO5rAPnORm\nMurzJoesVK7HzDWxVMwVEK8gzbjlHMr7Vhkrq3BhZKVS3CqW1FbjA02KrLQeWGT8tJzgkFE8SLs+\ncNi2th87Blx5pdsDlpbcfrBtmztuoktaKuBw717387PPVu+17Tf+viEEhZY5tOyLrJqDOecwVkGa\ncHg5wdYVbe5jDq3gMFXOIRulZhbUNuClkV525RwCDhxamMPPfta9btu2njlkwSETIWLlUrFkpWxE\nSvo5TgVpYjirgE1WGsocsrJSNmoPVPmGgDvnEEjHHDLPOpQ5FHB48WLanMMQ5iRFcCQk8DMqWSkz\n18SsIM9vZ8k5ZJnDlPJQ6WMIOPGBIsscsuBcq4yR+56SBa/fR2EsNcXFcoLD+ny7eNH5L319jMEc\nhoxlhqDQyErbfDsBbDt2OHC4bVtVfE0DDgFX6VQCp4AOHM7OAsePD9/bLmLDAg7ZgB0lK90oSBNu\nXQ5Fk25YrE9WWl9MpRJiqoI0sViCEHbNQrGL+ZvM3Bxw4sTwfe0Ch/ff715Pnhx2UFLISpmcw1Tn\nHLISphcSc5hSVlqf36llpU33XyKpAPDSl7rXlMwhU5mQCU6JMczh+fP2aqXjeM5hrOAIC060x7JI\nO3b8S1uNk9t0lEJfuy1bwgvS5Mg5zMX4Nkk9UwVH2D76/bS0ixHkSN1HYP09Gcecw1hS5xBZ6cpK\nBQ6Xl6s0gz17us869P356693YFJMCw7rzGEXaWA54onOOdwoSDMaY87GAro3m6Y2UiK/a+Ntm2Cp\n9PaMHCwEQPUxh7t3A08+6RhEsS1b2heQr34VePGLXSTJXyxjy0oZ5kQbkWWfd05Z6cWLzYdQj9tR\nFqEFabSOWUi1Ur+PIQVp/HXriiuq34nlzDlsYw5DZKUim7XKSnOdc6gBhzmDI03tUjKHrKy0iTlk\nGGYt4yhzQvP5pn5q14QmCWUqR1zaWZ9bk9QzlayU7aO0CwUn4ygHBpqDmOOec6iVlTLgsG1tl2Jr\nIiv1z/TVMod104LDU6f04DAVczj03Mo1+1EWaxewqdhgDoOMkSIB3QODAV3SLoZDkbIdO1E0OYe7\ndgGPPjoc7emKLh05AnzDN1Tg0JeVMsxh23djzzlkIoJM8ndKcCiFi6zOQSzmKidzaJGHXrhQAWZt\nO2FhpV1IzqE/3yTgtLDQ3S4Vc9jESGhlpU3zFOCZw1QFaSZRVso4gn3qFr+dFRyyCgtWVuozhyGy\nUkYym7qQTcx1kmFumeBnzoI0qeXAMWWlWpBdZ3ytQZVRyEq7vptFVir7QQg4bCMNfHC4c6dTr/lj\nOxY4pJlDQla6wRxGMI1D0TYwumSlTCS9DawxTlYqxrELeHX1U8scPvrosE7c3+Dr9tRTwM03N8tK\nGeaQZUUZtqWtXU7ppbafTdLSVLI69ruxbJ5/Pa0DORgMR+At7fxnwEaNgfXP7b77gPe8Z7hdrECT\n5lgchjnpYg7FGeiKLteNKUgTErnv+n6jYM6jRKkJMKpl5UIUFqysVJhDVlZq+aGbADkAACAASURB\nVG6jZKDGkbnKzRzmuhYQBs6tstKm9ZVlDlOx2W0+KCsr9cHh8nLlp2sL0rT1sQ8c7tnjjoXS5Cpa\nz/Zm12RGVrqRcxjB2s44FGPOeWvazNjoy7gxh01tNMnfddZErM4cHj06PDGb7onY8jJw9dXrmcM2\nkNc3OUNkpU1yKc3inZtdiAkOcxxlwS6ojFOnBZT161na+Q4ryy4D6+//y19eFagB4uW8ST+tgY5Q\nWakU3PGZwwcfXL9++MYyh8wz6AMbseY2m7uWU9YYyhwy918zv/25ZpGV+uudNvAzagYqJXPFykpz\nMoe5Gdic91/6WV8Xxk1W2rRGsrJSyTkUWansQbt363MO69bm39XB4dpaVe1U2sWSlVJS/3KNYw4j\nVCt9wYNDRlbaNTlZSVcsh1raMYwjwxxqFo/BwIFHP/oFNDOH9STiNnC4suLKHY+DrDQ3c5hLVgo0\ng8NxL0jDOKwWdsFfFyzt/DyokII0ue6/pZ91hyIGcyjg8Ngx4CUvAe68s/3vpTrKgjmEPRY4t4xj\nljkMXUtYpwdIKyut5xxqAzj+esfKSlPLGnMyV6ystGkt1xQ7YpmrGGBtHHMO29rlAochstKudn2y\nUqlQWgeHKZlDAYVacGghlrQBi1tvBX7qp6qfV0v7URYX1jaYw2Bjz+fr2mya2oRUfBon5lDkDUzy\nfRvI8JnDhYX1zGGXA3nllS6StLhYVWtsa9PXzxBZ6SiZQ5ZdA9IyhzGcY0anL06I1TmwOpBMuzpz\nGEtWWreYzKHG+WeYKws4PH7cFar6pV9y7zUZkyKQsyANs5azrFzqgjQxABSQXlbKBHDquYqsrJQB\n9ZMiK2UY35TfbRTMba6cT7ZdiAohlLmVPrLMYZusVAMO2wKEWlkpsB4cxjjnUHv/9+0DXvc67zpK\nFnAj5zCyXbiQpyBNSA5gKjkqA06kQEkK5koYQ4us9OBBByhPnXLJxG3fS9PPLlmplaVhmRNGjmdx\nRJiCHDGYQznKpS9yHMIcMsUnYjGHFnDo95OtVtrXNkS9YJUHyfWszIkWHJ4754oE3HorcOgQ8LWv\nrf98WeruCVtYwwoOQ9QcLDhk5w2zlm/damflWFkpw9wC62WllsCPtd3U1PqAaU5ZaUr5PXv/69/N\nUuwoNOdtXMFhfS/VjH8gHnPISKS1Qa0UOYd1Wakm57BtPU/BHFrAofb+r7vO2kUVC7hlagvOrbob\nupFzGMH6HjBzlEWbY2aNvku7FCAv5vXYhREYBicyITWy0rJ0DuT0tCs//MQT7pxEadPGHLKyUoal\nYZ+3NRgwrjmH/rU0R7lIO3ZsWWVu9X6yzKGVlYiRc5irWqy0s47l2LLSkyedY3DDDc3gUJyCvrzn\nGAwIkE5W2uSYMQxgSK6ipl292IvF6axXXUxREAgYBnlW6beVORwM8jNQMZjilLLSmLLGcStIEwsc\nppSVxrwnuWWlKyvtstK2nMPV1Yq8aLK2/cZXnAgxoQGH1nQj7Zq87jpKoFdnDjfAYaBZ0b/frmsQ\nNhWkSZVzmBtUppI1vuEN1e/EuqSemza593fvdoVshDnskpX2FdupOy9l2e9YxHbEU+VXhORchTKH\nOeVSWoeu3k8LqGSZynGXlYasJSllpQIOX/xi4OGH13++T1Iq14t5lEXX94u5tqaWlTLzlAGH7Dl7\nIbJSpiANU+V0FDmHMZji1LLSXMArd+5mjCAmkF5WmmtMssyhVlYqCr8uWWnfHqBJG9q0yQFEHxwy\nRSnlb8ViDjUS0RQFacLh5QSbtRytWNfAiJlzOI7MIZNfBPQzh1u2APff7yqQirUxh+I8AhU49JlD\nRlYqzovvEIgT2MVIjMIRHwfm0MKc5MzlCGEOLewCcz0rOJT+1SW5rKwxZcAihax0ZWUYHF5/vTu2\no259xWgAPjjSJv9OwRw23UdGVmoJ2NXnm6ZoCAu85L7Id2Kl1VpZKRPA2bzZsRXa6wDxJHypC6nE\nlJUyBWlSykrZa/kAJUdBID9nOjU4zCUrbVsjQ2Sl+/a56z73XKUM6ZKVhhxL54/ld7/b7TPWdn3X\nY5lDi6w0dkGaFzQ47HvAsWSlIbrtcWQOU4GTl71s+P22aM/ysmMVALdg3HdfOHMoffSfLVOpEeAX\n1JQSmpyy0pAxEgMcWpzVUOaQlZVqHKzBYH3AAhg/5jCVrHR52RWjuXDBRXP37XP5h3UbBXPYBw5j\nBOy0ziMbsGuSf6diDoH18ztE1tt3TfacQwZU5magQq7HgAxrcDC0j7mYw9zgfPNm4MyZ6ufcOYeT\nIiuVYJ8EamSdnp11xQebvr8GHDaRBvVn8OEPr2/HMof+vEmdczhVTKEsS6yurW4UpIlh1gesadfm\n9DPOkrZdDAmTtl3Iwmh1zpoWHWA9c3juXHjOoVzPugizuYMxpWe5wWHfppY7B4qVedZlpQxzaGUl\nLDmH9WuJWcE5kD9glEJWKmxi3TTMIQPy2HajlpWGMIcpwWHT+spUK9UyXqEFabRrQiyZZ25ZKQvO\nNe1yM4e5weGocw6t6yv73UKIDVZWKjmHEiAUP2UwcH7e6dPr28RiDi3tuuZAfd9OzRwWRfH1ojQb\nBWkimPUB++0mPecwVn5RSnDSFu1ZWRkGh0B4tdKmPjIRUu21mu6/lnEcB1mpBdSPM3PIgLw6c2iR\nlUo7zb2Xa1mdsxD1Qox2obJS2ejFgZifd06BOAttn++ymLJSDXPIrsl+3jMrK03NgLCsXH1d0FYr\nzZ1zaC1Iw+6JIQAqlqyUlfVaFTXMHiDzwFrlOnXOZ0xwqB1f1nax/LRUzGHX+u/nHC4tDa/v09OV\n7Ns3FhwyeynTjmUOLRLR7Zu3Y+XiSrScww1wSDCHVllpanA47iwBwDOHfbLS/fvdqzCHTW3KMl3O\nT2xZr/V62vufkzlkx0gIK82CPIY5ZMGoNeewfi2xVBV02THJbIRdzoF8v23b3FyXYFAXONQwh7kK\n0rBrcr1oixYchshKQ5lDdr5JW0a2r7mmrFuaomL1dswxHTHuf2rGlw1+MsxtDOBrabO2VoFJy54Y\nCxxqcnXZgjT1PVgznmONyVQFaZr8CrE6OPTX6bZcxRBwyDKHuXIOtUBvevM0li4sRcs53ACHJHPY\n1q6pDUvNs/LQlLmKMRx4MaYkPzAsK/32b3evXcyh9igF1snNnSsaI2oM5DvKIjdzyMjcrNIzpl0s\ncJhKVhor0BHKHMq9mZlxOSZ1mVHdzp0bL+aQXZOlrTUPNoQlYMGhOGghslK2WqnmuQ0G1X3RSriB\nYeYwtayUye8KvR5z/xlZaQzgpW3jF5OzXiuW9JJhAC2pBaHM4bjJSqV/fnV4sTZZKRAfHDJrOdPO\nsk6KlWWJi2sXMTXQNdyxeQcWzy9u5BzGMJZS7pOVxorah4DKlDlvrAMfkzkUcPiqVwGf+IQrViHX\nYRzcpuuFgDw2x3TcZaXCanRdM8QxYAMPvrPKyNwsi3eIrNQqdWubNylkjbFApZZxb4sc+wUIzp7t\nB4csc8g4WUD/GGPXZGlrDXTkZupjFaTRMoeMrBGoWEB/v9C2kevkBoepZaXW+z/KaqUWtoUBlbFk\npdoAIZtz6AcspF0qWSkTsGjzXbvaDQbDgN63PuawKVcxxlEWTRYLHGrHlm9r5RoGxQCDQtdwx+Yd\nG8xhLBs3WWlMKdi45Rwym0yTkwUM5xwCwLd+a8UKst8L4GSlsUC2tl2sfBOAA4fjVowAGK58lkNW\nGlIJMYesdNTMIbtuiVnB4Tgyh8z9r19vnJlDNuewzlylOsrC76eMH4356x0rK02d8zZqWem4MYf1\n66Xeb5r8hFQMoLST+aZtl1tW2hRA00q/69aVc9hW5XTcCtLEYA6th9lPb5nG4oVFc7s2e8GDQ+Yo\ni66B0bSYjmNBmpjgRLuBWpnDLllp22Y/GFQ5hn4fmc0zZc7hpMhK689NG+3P6fT4G4Y1d5Cpcrp1\na3glRC04ZJzjWKw0wLHnWllpHzicmdEzh33gUHL5/HVh3ArSyPWsbHZIzmG9nSZ3ih3/TYxLKlkj\nUDEu9WBiX5sY1UpT5nzG2gNCjhLJESC0FPFgcxVjMLcsc/hClpUC7f61rMuxZaVN+03KgjShR1lY\nGUBhDpcvLGP7JuWC12EveHBoZQ4lwb3tQY8i53DULEFIzmGorLRuRRFv0deCQ8ahjiUjtrAETO5O\nbuaQaSdHHgB2sOazNCxzmDvnkC1Ik1NNwMiqxWSMzc4CCws65rBPVloUw9csyzBZIyMrtc4BVlbK\nMlfa68U855A9ykIrxxNZKcscppSVhjjwMdppvl/uaqUsc8i0i9FHgGf4LczhqMChBtTI3/UDb9o9\noO2ouM2b8xSkCWEOUxekMTOHm6exeH4RixcWsWPzDtvFGmwDHHbc+6aFUSZZW4S1LWrPgIxxZA5D\nNsJYzGFfjlFIHxlZKSsHjtFOG5FiZXVMnkRu5tDPQwjJAWTaWa8X6nhK25znHFolouz4F5PvV5eV\nSvXSummYQ2B9MGBqyl6kSvrX9f1CZaVW5pB1OlnpU0xZaUrmUJh6ljlkZaW5ZY0pcw5ZWWmMvEgt\nky3tGOYwFrvGgjytemSUOYep7kmbrFS+344dbr3313c25zCEAWRkpXV/i2EOrUdSCHO4eH4R01um\nbRdrsA1waGQOU0m6pqbc5/zqTSwDlRJUMhIyIC5zyBylkCqSO2pQnyPn0OrQjYI5ZGSlvswzBFQy\njGPKcw5zM4fMmtA2t4H1OYfC/Gzd6trU55umIA0wvJ5rAUZuWSnDHIqU3po/yzq5OY+yYJkr6eeF\nCzxzGCIrTQ0OYzCVrKw3NXPIqDkY5pBV4TTJoxlZqXbviFGQhpU6p1Qh9KVtbd++njmMnXOYqyBN\nNubwwiKWLixhevMGOAwyBuhposZMzmG9HLO0S5XzFotdSJlz2AS8gP7NKRY40TKHo5QDhzgUmmfH\nRDtzR8RZWSnD5Ek7ppQ/Cw5jMO451wR23ojJ99uyxa2L8/PuGRdFM3uoKUgDDDsjIeC8z2kNZQ6t\n4BDgWWnGyWWZ81jMocWpthakicHu52BpYqh3NPefLQgUg5VjAw85cg5jMIdWBlxMc19yykrleswe\n0MUcbtni/s7y8vgdZWFlHFnmkMk5XLywwRwGW98DbssftA6mlJtFSK5iDJYgZc5hmwOZkjm0LnBN\n4JxlTnIWpJF73yfbYSUtOZ0ecQLX1vIwh35BDhYcatkuhjmJNbfZdqGyUn9dmJ11zoA49015hxbm\nMAY4T8kc+nPHErDwnSZtOyYYBoTlHNaZq1SyRoCTldaPstCyLTFYmhyy0lz3PwY4YSXLOYBQ/T4y\nqR0pZaWswitWwELLHLaBQ5H879gBnDnTf5RFX4CwS4V2uTCHQ7LSDeYwzFLIStvyFFNtFrllpSEb\nU6xzDlNNzKZ2LHNobVeW7p8mByqGY2CpljnuR1kUReUcs8yhxRFhZaW+46llM3LKSpvGslbabp1v\nWnA4M+NeBfxt377eOdAyh/7zDmEONeCQuY/SlmUOmfP5QmWllnnTtAalkjX6/bTISmMwh7llpWxq\nh+b7sbLeGKycZS2vg8pcIFsqozPqKe34qu/BmjkXa0yOUlYKOHB46tRwzmHMaqVMoJVpR1UrXbVV\nK53eUhWk2WAOA63vAbctjH2yUjZqzDKHuWWlMTYmgGcOGVkpEwFmmUOWASyKfnAYS1bKgkNttDln\nLg1Q5SKEONRMO/Z6oeAwhay0PrYkYJFCVqrJOQSqV5kXbcyhVlZqdfz9ZyamCU6FyEp9J9fCZocy\nh4ys1DJvmLWkbd/QfL8dO4DFRfs5hzGOskhdLTOXrJSV9bbtb31WB3kMK83u9xalih9U7CpQ6Bub\ncxhDvcPmIYfkKrKyUr+vs7PAyZPpZKWpCtLEYg43T9kK0iycX8DyheWNaqWhxjCHjKw0ZUSQjfbn\nlJBJP2Mxh7lkpQwDqL1eyH2MwYpeTswhUOUdhshKLcwhk3Pob2wh4DCVrLGJzWYDFjFyDgFguhYA\nbQKHmqMsAI45bJI+9QGitvtvdVhzgEOrQwfw459ZS0KYw127XL5q19FHdaszhynX5BCQxzjwDHMY\nIitlxlaIrDRXziGbjhCScxhakMYikc7JHHblHALA3Bzw/PPpCtKEMIcW7JAj53B68zSOLx/Htk3b\nMCjCod0GODRKRPvaDAZVsQ+xEL03y1ylAjUs8GKrlcZiDlOB81HIc2PkSYwrc8iCQ8lFYGWlLHNo\nkZXKuU2ADRzmKkgTy1mNKSvds2f4vVDm0JpzWK8UqGkbyhzGkJVq14ScslIm56pp/Gvn3K5dwOnT\nPHMYIitN6YiHMJUxmFutrNSqwpE+MrJSv10OcMgUMmOPsqirFxhwOI7MoUZWOjcHHD8efpSF9M8/\nDQDgmUNruxw5h3t37MVjpx+LIikFNsChmTnsW7CKgj/jZFJyDmNsTMD4FaRhNrRYzG0OSdEkMIch\nstJQ5tDC0jDtpqedzA0IZw6tjHvKgAU7bzTgcO/e4feaZJ7agjQsc1i/HqN4uJxkpVNT1dEZVge+\nvpaklJXOzTnm0FqQZpRHWaSUQ7LMIXP/Q2SNfnDEwhz6stJc4NDKHPp9BOzXA3Rzrok5ZMEhEyDU\n9rGPOdy5062/obLSomgmG0IK0lhS0izj5OvXMILDQzOH8PCph6MUowE2wKE5apAzGXgccw6bHEFm\nYxJ21SrPAuxSgNzVSplqjanz8mIyh1bHIAdzKHITlgG0ykoZcMgwh4xzzErbYwUe2GrJYl3MoQ/M\nxSxHWVhZoSbpE6N4YIA2Cw4toIaR/vkFoLT3Hogjj5N2Wlnp6dP2gjQxmMPUBWnYdtZAn/zduuNv\nDRAyskbLWs7ISkNUUEwhM//+hzCOFy70z7lY4JCVSMeUlcpnAR4cAlyKhgbAaq5FFaRZsxWkOTRz\nCM8uPBsl3xDIDA6LoviNoiieKYridFEUR4qi+F7vvduLonigKIqFoij+V1EUL6q1/bmiKI4XRfF8\nURQ/W3vv2qIoPl0UxWJRFPcXRXG7pj/MURbMhpZysWIdkfpkDqmWyeQcao5SaJOVWqs1hshKc8lz\nQ6THrGOQSlY6CuZQ5CZWKV6orNTiHMRiDlPJGtkxGSuoArg1yF9jm5jDunOQkjkchayUYQ4ZUMNK\n/4BK3ru87IIeGmtycjWyUpY5jCErTZ1zyK7l9XVSWxDFKutl27GBh3rOoYWVZphDZr+JUZBJO4br\n7QAOCDHgHBgPWSkwXLWaBYdNe87ldJTFoZlDADCxstKfAXB9WZa7AHwrgJ8siuLWoij2AvhdAB8A\nsAfAFwD8F2lUFMX3X/r8KwHcDOBbiqL4Pu/vfvRSmz0A/gWAj136m52WQlYq7djNwtqOZQnqi3dI\n8QmGOWQdA0DHHMaQx4XkfI4TS9PULrWsdBTM4SgK0licA6meCIynrJQZ/2y7Nsm4rHnS19e9bvj9\nWMzhOMtKQ5lDliWwsBkS6LAUe2GZw6Y9WPPs5uaAEyfcvdd8HqiOSllbC5OVMjmfITJubT+ZaplN\ne4eVOWTBoUVWOoqcQ+t+4wcjtWOyCRxac3Vzs+DawHobKydtBRwePuxeZZ+vmxYw1/ccqwrNb5eK\nOfyu//Zd+MMH/9BVKx3oq5XObJkZeg21rOCwLMv7y7KUR1sAKAHcAODvAfhKWZYfL8vyPIA7AdxS\nFMVNlz773QB+sSzLZ8qyfAbALwD4HgC49JlbAdxZluW5siw/DuBLAN7Z1x+rbhjQy0qZSkVM1CYW\nc5iyaIu0CwUZ2rYxZaWpqsWGMLAMu8OAc2mXqyBNaM5hyDmHIcyhJeeQKUjDyEpjMIejKEhTH5fv\neMfw325jDlOdcyjzxi9kMO6yUosj6DvUgH6+zcwACwth4JDNOdR+v127gKeecv3TMGuAu9/btrnv\nlcOhDt2n1tZ0x80AzXsACypzMIeWNbl+T3IWpGGYQ0sgpp5nzchKLTmfMRRNIbJSv6/yNyW9ILas\nlFEPatqFMIcX1y5i/ty8mTkUu/16lXCy17LnHBZF8eGiKBYBPADgaQB/BOAVAL4onynLcgnA1y79\nHvX3L/1f3ns5gEfKslxseb/VWOawb5I15delWqxG7QimZg6bwKGVOUkpK2VzDmNKLxnp07gWpGE2\nJqCKKGqBAsAzh/WCNKllpUzOW042m2EJtOAQGO5DE3OocZaA9ZJBzfgvCrskm1Vz1NvmlJVaCybk\nZA5DZKX33gtcf72uf2Kzsw74srLSHDmH/nPTrj8sm8QUsmGZw/oZghbmkJGVxmAOGelralkpU+EU\nGL2sVApdSdsTJ9yrBHeaCpIBejbVqsIJUa+xzOH0ZneYPQMO//f3/G/88Bt+2NSmzbKDw7IsfwDA\nDIA3Afg4gPOXfp6vffQMgNlL/6+/f+bS75req7dttb4J0xa1tDo+41qQJjc4Cc1dE7MyJymTqlmW\nIJY8lG2nWUyBvMwhG20GHGiw5kCxuSNsO78gjbZIBsOcNG1oLJvNjkk257APeDUxh1rHp84css4Z\nwxxq1yA28BDKHFquBfDgMEZqgRbYHzjgqpXedFP/Z32bmQHOnuVlpalzDv25Y2Wu6iBPcx8ZWSm7\nljM5nwAnK2X30rrihJWVpgSHTCAGCBuTMWSl0k7A4MteNpxP3sY2pspfDpGV+v20BLqnN09j8cIi\nLqzaCtIAwJuvfTO2blIk4CvMzllGsLIsSwCfKYriuwD8EwALAHbWPjYH4Oyl/9ffn7v0u6b36m3X\n2Z133gkA+MpXgCNHbgNwW+Pn2gavNdrJFnbIWXV0HJnDNlkpwxyykk2GARlH5nZSmENGCgNUwGtp\niTvwenVVV9RE2oUcZVGWjjnUXK9tQ7Pk3EqxqVxrAivHBvq/WxtzqGVArDmHwPqiNGxBGi1zwuSz\nhh5lYbkWwMlKGeaqjTnXfL+XvtS9ap+zmHy3HLLS0D0gB3PIBnb93DBtP+trMsPKpQbndcUJKytN\nmXPYBA5ZBjCV6qoJ6NXH5HvfC7znPd1tpF1u5tBCLJmYwy3TWDi/QMtK++yuu+7CXXfd1fu5kYDD\n2vUPA/gKLuUQAkBRFNNwuYhfufSr+wDcAuDuSz+/6tLv5L3DRVFMe9LSWwD8ZttFBRzefTfwqld1\ndK5lMFk3NLawg2ZihjCHMUBGSM4hW5DGyhymlpUyLAHzrJvajaJaqTVqzIIMi8O6c6eL9luZjNCC\nNIys9MIF970YxxOwB0e0xaZyMu4WWalvbcyhtfKoBRzWi9KkLEjDnqHJsNn14h+5ZaUhOYeaZzcY\nALfeur6oUZ/54JCVlbI1Bqz7lJWBsjKAbDt2LWcrQdeZQ/b+a6u+stJXkU2yygVtoaS6/JLNOcwt\nK20az/4z6QKHKSTqIT4o498BjjkUcLh5yhjZUthtt92G22677es//8RP/ETj57LJSoui2F8UxbuK\nopguimJQFMXfAfAdAD4F4PcAvKIoijuKotgK4IMA7i3L8qFLzf8TgPcXRXFlURRXAXg/gI8AwKXP\n3Avgg0VRbC2K4u8B+Gtw1U87jUlGzS0r1VwrBjhJnUQfmzm0TMyUktk25tD63NjcQSZPAkhbkGYU\nzOHsrAOHS0s2WWloQRpLOymas7SkL63PbmihcjVpxzDu7LwBdOCwzhxqHWQm51DahcpKLZI1P/DA\nMIeMHDJEVhpylAXj0FkA0T33AO97n+6zYgIOLzdZKcMASru6HNWqHrHkHDJrq78GpZRCAnxBmqKo\njoCxHK9SD2pt2aKrKg9U328UslJGFWBl5LTtpI+MCoeVlbLM4cyWGSxe4HIOY1rOK5dwEtJ/CwdK\nHwPwnrIs/xAAiqJ4J4APwzF+n4MDjq5hWf5yURTXA/jypb/zH8qy/A/e3/4OAL8O4NSlv/vOsixP\n9HXIqhsGdIM+Z0GaUTCHLKhhmENGehbCrjHMbe6cQ+a5WaVxYjEcuhzM4eysyy9aWXG5RhqLUZDG\nsskPBs45OHnSBg5DC9KkHluxFA+ATlZaZw61UXg25zCWrDQlcxh6lAUjK52fd+1ZkM1KwSzPjjG/\nIA0LDrXtcspKmcIycr1c1UrZNdlfT0Lk8BYmVVhAy1gUcHjuHHf2prXgmqhUWHDIBjpCZKVd7Zr2\nQ4Bjs+V6OQrSmNJPtvAFaWJatiuXZXkcbcl97v1PA3hZx/s/CuBHW957HMA3WfvU94CZYhBA3khi\nGzixFoRIncTNMods1CYG8NXK43LmfLJy1KZy2KlkpfU2KeWJYrOzwJNPuuvmLEhjkdUBrm8nToQx\nh6nGf8iaUM8vSskcLi8P/45hDsdVVhrCHFqrldZBhlVWevy47ZgIRr3Qxi5Y8wgtJgVptGuCPFd5\nxhZZb2iKQAhzmFJWmps5rEukmfVOez2/grE1qMIwh/68sYBDuZfbtnFScyC/rLQPbMfOOUxZkKY+\n/rXPTQrSXFy7iE3F6MBh9mql42QMc8jKShnJIFvYISVzyIIThjnskpWmYE6YDa3JEWSeWwi7pmVN\nrGclAesX43Pn+gupxKqUxshKmRyoevnsPpNz0NbWqs1Xazt3As89Z3MMQqUwuYsdsXJ4gGMOtWAh\nJOcwJ3PIgMN6IRvtfAuRlQo41BobaMrNHFplpQAn0WUlfOxxD7GqlaZkDmMVpEkFaMRkLFuDKj44\n1BZAq4NDZt1ix2RK5pCVlcbMOQxhDlPJSqUgzfnV80lyDrW2AQ6N+mZGS53SOWOj1DGlZ1q5iJU5\n7GIXrBMzVUGaNkcwF3MbAg4Z5lCzqbGSiqbvZmEOrTmHU1MuCry6qr+PQOU8yr3QMicAsH8/8Oij\n7m9ojNnQcrPSTDt2bjflHOaoVpqTOcwpK5V+MrLS55+3g8MYe0AO5lDmtzba7/eTZWm0862eO2sB\nsLGqleZgDq2yUisLHgMcWu4/4PYmJudQ5rY2qOv3EeAl0iHEBisrZcAhlEVZOAAAIABJREFUm7+c\nsiANo54CKuZw4fwCZrYoHYUEtgEOCebQGu1MGYEfDJxzurZmu94opGe5mMNYrJz2/pdl+P0PccSt\njjHAg0NNrkSTrDT1Zr1zJ3DmjF3mKZFqy7V859HCGgIuH/LLXwYOHdJ9nt3QcucTM1FjJpejjTnU\nBkiszhIwGcwhIyv19ykrA8KAQwZkMLLqUBNZ6eKiY0g1Fos5tLJrFlDvP2+LWoKtVupfy/LdQgvS\nhChVcjCHS0u2nMPt26s1gZGVArY1gSE2YslKN5jDKufw7PmzG+BwVGZ9wNLG6vhYpJcxIv6pmUOm\nHRs1bpOedT23nAVpmvrJFqRhHHFLlFpYMkAPDusOHSMrZQs0MLJSC3MofT1/3n4t6xlvYvv3A1/6\nUjg4zMEcpqxy2pZPnKtaaUpZqThKZVn9jskNtspKJe9Tez+YCqdiBw86BjxEVsrkHJZlenA4N+cC\nTSw4ZMEJe84ewxzKvqFRPYTKSmXsa4+JCC1IY1GqMCkawHBQ0TIWmZzDbduquc0UpAH0fkLdT5sk\nWSlTwyIk59BSr8TCHEq10oXzC5jdMqtrlMBe0OCwb2CEyEpZxz+XHDUWu5A655CZmDFZOaawgOZ6\nTQ61ZvNkmUNg2PEMYQ6ZnEPmPjKyUoY5PHfOlse0davr25kzdnDIMIfWghwxmcNUa0JX4KePOWwC\nhylzDq2yUilYwewBLHO4ffswONRei2FpAODqq4Gnnsqfcyj3xCLlttquXcDp02HgkAEnTM4hyxxa\npLmhslJrkIP5bnVZqVVWbe2ngFjrvGFyDiXHHUifc8j6oCxzaAWHLJPX1taav8/20+KnzWyZwdlz\nZ3H23FnMbt0AhyOxVLLSEJDHRm2sEf+YLGWqnMM2WSmTc5hKHtfUjinIkSMvb1TgkL2Pls3aZw6t\n1UOXl22gsiic9Oz4cbusdP9+53RqwSFTkCOnAqHteqysVMMcskdZ5GIOAf4ZsMyhOJ3STiuzZYAo\n4MCh/6ox/z6urTkW0DpOUhejARw4nJ/PIytlVTgxmEOLrJqRlVpzAAF+/NdlpTkL0qRmDv3ATw5Z\nqZUlBuLKSrvaheYc1v3JXLJSy3w7MH0AxxaP4cz5MyNlDhMvs+NtVmpY2lg3NFZ6yZYRzl2QJhVz\nyFL6MWWlDOPF3P8c0ksWHPr9TAkOQ1jRvXvdERGbN9tkpf5h3hZQOTtrz7kCHDgExlNWys7t2AVp\nGOZQC4ZkXLKHUGvPNmP3AKawDDAMDi2yUgZkAMC+fe715S/Xt2ELZNRZq5TFaAAHDk+dsoNDK+PF\nFqQJyZ2VMWkB2YysNIQ5zCkrDWEOpSCNJagiwUhLzqEvK2UK0ogUm1GvsRLpcZSVtu2lDFNpbWeZ\npzs278D05mkcPX10I+dwVMZQ2JoFi9UbNzkUTE5AalkpC3yt4JCNEsV0clPJg1gGcDBwf18K4Fhl\nRaNiDhnga/luO3YAe/YAR4/aANv0tGMbreCQKcgBALffDrz61cDNN+s+Xx8nGnCSE+Q1XU8zb0Jy\nDtmjLPzxv7JiO/KkLr/sW5dZ9QjLnOzY4cax9NHKHFqdXPkuV12lb+OvC2zOcw7mcG7OHTdTFDZH\nnGEOmUBf03jUWB3AsrLSlMxhyHezykpDpH8hBWkYWWlIzqH4kVY/zQoqrb5Tk3/X9wxiFqQRH6qr\nnyxBEQIOAeDqnVfjyPEjI5WVvuCZw1SyUlbWyAyonMxhiANplZWOgjnMyZywkhZpOxiEMYca57i+\nGGsYlyZpCsscWjbea68Fnn7asXpa27GDYw5nZmznFYpdeSXwhS/oP98WROgCJzHHP8ucW2XVYgxz\nqAUMPuOyvMwdQs0y7hZwyOYcytmbFpaSAaJin/oU8MY36j9fz3ljZG4W5oS1XbtcPqWWNQQ4UFPf\nt7XtYpxzGCIr1bQdJXNouf9ANV+YFA2r0y/VSq0FafycQ6uslB0jcl9SBQhHXa3UWnXXcr1QcHjV\nzqvw5ee+vFGQZlSmGYg5ZaV1MBqSq5WrII0l53DcmUNWZsLckxAgxORXAFxBDoY5ZMcxG0kXe/hh\n9zo3p28TIiu1HgLOGCPpihXkSF2QJiZzaGVcLM+bmTchstKQnEOZMxrFSUjOIeBYcEtwxF8XtPfR\nv/eAzTlmbdcu58BbwCEDakKYQ6ZoS/3+s7JSDUCv71GWAGaMgjTMPWFzDhnm0CorPX/eMXlMQRrL\n/a+zyyHS45yyUgbosVJUTT9Zea7YVbNOkjFKWekGc5hAVspKiliZAxO5j1l8gmUONfJcRl/O9pEF\nbGzOoRRlkCMmLBuhVcIETEZBGoZxFPu1X6vkN1oTOZ6FSQJ4WanVWElXTuacCaqw4DAk51DyfQBb\n5J6RY7PPIJQ5tDghzNmIIVYHNVpw6D/vHOBw5pI/ZlkPGFATwhwy7BrLHPprkBagxGIOU8pKgeqe\nbN2apyCNrEGW9UfkzVJV2yortbLEMcAhKyvV+OSrq5Xf5LezjEmAZw41Z4QybLtvNx90eScb1UpH\nZBoGipGVsjmHrByPcepyswtNZcy1URv/vDCg/7nFZEBSFQSSHAAmuhrCHOYCh/5zC4mkW8Dh294G\n3HGH/vMAzxwKOLTKSq3GVvmNERxh24VEZNlqpZqxPD3tzqcEbM/bLxLD5soxBWmsOYdWcBjKHFqt\nLs/VrgnijAE25oQ1eU5N47PNGMDGBoNDHHgm57CeT1kUNtn4uMpKpZ8Mc+jLSlPnHAJV3iEjK7Xe\nDzaAwIBDq6xUxp61XVMf2dQmTWpHqKz0m2/8ZgDYkJWOyvoWyJiyUjapl2UOc1UrTZlzKHr3em6S\nNroklkNWGsqmWqOkDHOYqyBNfQFngxzWqCxjUpDGUqAEcNUaH3tsfGWlTHAqltRcsxFKYaWmwE8q\n5nBmpgKHluftVwLNkXPoS1+1lXd95tCyjqytuWc3CnCoBSd1WW9q5lDsySf1n42Vc6gZx/UjCixs\nV6jjr73/LHMYqyBN6r1UwJp1j5qZcefjWphDoJrfTEEaptAOkB4ctpEvDONoLZIk17KqAKWddQ+2\ngsMb9tyA33vX721UKx2VMbJSJkLBgpNJYQ5T5Rw2tQPyyUrZo0QYZ5yVlaZmDkX+KpW9NOAQGH5u\nrDzXGpVljC1Ic/gw8NBD4ykr9R0sgL//2nHc9Nz6+igBBGvgJyTn0AeHFhmxXxCCkZXK3NHkAfrM\n4dKSDRwuLdmcEJGrWYtWsMY6nv54ziErBYCXvax6bhpjjpdgZaX1tZVhDq05h9JOy9yygc96sZ3U\nqRZ1wKy9ngQVrXvUgQNOcWLJOQSGmcOUOYcxpMdAOllpSLsQ5tAPYjLXsoJDAPi2l34bCs2mkche\nsOBQU6a3KWqgzTlkwSFbyGNUzGFIziELDq2yUpY5CWFcrM8tR34FU1ijKIafgTbiyYBDVtYbYqys\n9PBh97p3b5p+idULcjCFnCwyN2YcMxFZaVcPvlmZQ03+h9jMjHvWgJ059M8ZY8AhE8BZWrKzm1aQ\nJ2A0RyCmfl6bhTnMDQ5/6qeAd71L//mcBWlCmMNQWamWOaxLUa3MofhnFlkpk2rBykolV9063w4e\nBI4d42Wl2uAswFVUrUuPU8tKGf+aIQya+qhpMxg4H8gPFLFANHXwLba9YMGhpkxviKy07pwxOYfs\nJsOcszeO5xw2XY89542V9aYC5/V+shHg1MwhMPzscjKHOdiMHTuA+Xn3vCzXEnD4Td+Upl9i9YIc\nmo2pDigtMrdYiodU4LDOHMq1NAFWljlkZKX+HNWeVwsMg7XVVT0Q2rrVXe/cOds49pnDnODQ4ngy\nQa1Qu+MO4Ld/W//5nLJSljlkCxD581TLXLGFZWQdlvGfWlbKglgBhwxz+NxzwNmztmq4MncWF6uC\nSX3mM4dsAIG5jwBHosg1czKH2vlmbReDORy1vWDBITOYtO3YSkUhslKrUxdLVpoy5xBoLmTTlwwc\nq7AGy3gxTjW7gFuZw1zgsP7dGOYqB5sxPe2OpLAWlhFw+NrXxu+Tb3VwmJs5TCUrbWoH2JlDy6Yb\nI+dQyzCz8iyfyd6xQw8qi8L1a2HBNmcEjOYIxDAgGxiNrNRqzNmDrKyUZQ7rzC0rK9Xc/3ofLWNL\nnjdbkIYNtFraSTqCNahy4IBjDo8dcyyi1mTuLCzoQSUjK2X27Xo7IL+slAVsDEGhxQ7WAozjZhvg\nsMNiyUrZwiYpmcOYstKcOYda+UCMwho5c7Usm4yfl2QFlZPGHOYCh9bcwR07HIudesFvYg6t4DAH\nc8jKSus5hyxzqDE5L+ziRZ451IJDfy23OJ2zs45VsOQb+v08dco2lgVs5wjE+PeRZQ7HGRxaARsb\nxGFzN1lwyMhKWebQb2uVhzKy0tCcQyuTvXOn+25Hj9rAoTy7hQU9c8jkE4fks+YEh/V9ijm+TTsH\nGFApbSRXcYM5nCDTDAwZbHW9sbVaKQsOLcxhroI0seSobNU/JkIU8t3Y+29tFwIOx5E5ZMDhYOAW\n05wL6s6dwLPPpi8sw1oTc6iNrPpHibA5nymZQ1ZW6n83i5NbFFXeYWrmkFUFzM46B5AFh8eP29oJ\n2M4RiLmcmUO/jwzIA+ygUpNi4ZvMnbU12/hnZaXsMSk+m80WpMmVc2ipKAy4NWjfPrcG7dunbydF\nsSzgMHdBGqvPyyrz6qSNzIE+lUV9f2NTmzR9rFfa3wCHE2Q5KWWLrGvcC9Kw7UJyDq3MYayqi2w7\n5l5ac0f8jZeJwFsS23OBw3oFyxwO64teBBw5MlngUJNE729MFgVCjOBISnAoBZJ8lsYSuRdpqaUA\nkV+t1AIO2Qj85s3AiRMcm/3887ZcJmEOc8hK6zmHTEEaLXOV2xjmsJ4brH0GRVHNHUvgoSjc815Z\nsY1/fyznYA6ZMzvr+wYDhhhwaCkaJSZFzCzzbW7O5cYvLurntwB0KwM7yoI0mr42BVVYBjBlO6aC\n8TjZCxocsoCN0TZrI4KMrJSJ+MeUXjLFdlhZqZY5ZOShsdox99KygLOyUn/Dtpyz5EeqWXCYOneE\nteuus0d/cxojKwW4wgKx1AQpcw6BsHym6WkHDi3j369WqnUGWbka4NjDY8fsY3LnTuCZZ2zgkHEg\nWRNZ79qajfGty0rHMXeHyTn0gxwApwKxrpEC0K3rP8MchoBDqQSaWlYawhwuLnIM/zXX2D4PAPv3\nu8CPhTlkQDYrWR5lziErD2WZQ0shG3afGgfbAIc9xhSXaQJ5KWWlo2YOLd/Nl4OlyjkMKSzDtItR\npIeVlVqZQ2lnYQ7lelJ+25pPY92sWaeasf373esNN6S9DmuMrBTgwHmsNSElcwjwsmrAOVbz87Z8\n0bqsVOMMsjmHgAOHzz5rdzrn5oCnn+aZw9RzzWeuLMzhJMhKY+QcWnPlrJUogQocWplDHxymZg7l\nzM6QoCKz31iuJzmHTGDx9tttnwc4cMgcb+P7aaOQlWr6yviETddL3W6DOZxQC6GUc+UchjBXmj7m\nzDmsSwZTRm1igryUOYe5C9L4si7LIbwixbNESRlZqbRj7glrkqfw0pemvQ5rjKwUyM8c5ipIA/C5\ns0AFoCxSsJw5h4BjAJ97jgOHzzwzvjmHQCXRDWEOxxEcMiwBKyuVtiHMYWpZqV9VmGUOrUHFUFmp\n5XohstL3va86b1VrAg4tslKGOfRTEiaFOWR9+ZCCNNr5PcnM4YR1N57llJVa5KFMu5zMIetASluZ\nkCmZw5jy3FxVHnPkHMqGBtgO4WXAIcuK1gsZ5FhQP/lJ4PWvT38dxphzDoE41WIvR+bw0CHgy1+u\nGGONseCQkVUDlazU6nTOzbnvdsUV+jZyLy1KghATiS7LHI5zzmEMWSnDHLKyUu34YmSlMrfX1ux9\n9MEhwxzmkpUuLbnnbg3iFIW9DcscMmcxyvNmwaGc69pXIGYU4DAW47ghK72MLURWqmHlGLkI285n\n5IDxPOcQWF8SO1XOIbvpsqxojHtpWcBFmmVt54NDizPIlNdncw7ZM7xC7K1vdd9xHK0ODrXMCXP/\nm4IjqZnDOjjUtA2JyF5xBXDPPQ4kai3nOYcAn3PIykqtTFKIyb20Moe+rHRccw6t69bUlHOgmYqG\nzHEPwDBzqFWO+PNUC86LogLMbEEay5hkC5mxSpX6eaSpbd8+XlZqndvih7LgkFXKAbprxmIAczCH\nG7LSCbSUSaxszmEsxmsczzmUtr48hTnKQsscxqj6qp3Qk5JzWAeHWueAKa/P5hyGyJEuR2sCh9aC\nQNYgk+QFW/OJxdh5A9iZQwvIABwo/Ku/srFrfrVS7RwIkZWGgENrQRrGEQ8xFhxOkqzUKmsMCaLl\nkJXWcz6tVWZZWalV+sqA7FDmkJGVMuYzh1ZZqbVoDsNKM/cxJnPIEg0pC9JMuqz0BQsOLZSydSDG\nyDksS46BEsdOc+YLCw6ZdgDHHNbvv+WcN7GQgkCaPjYxt9Ycrxw5h7JZAHZZaShzyFRUzSUrHWfz\n5VlAWuawKNxclmtZ1gRW5hOac2iRJwIOFD72mJ05FKaeyTlkZKVPPWUDeQCwa5db/y2OIOOIh5gA\nbUtw6nItSANU303OLLTmj7PMoUVW6ueqW+4/Cw5FDmllDplAK8vwz80Bp05x1UoZu+Ya4IknbOBQ\n7uPSkm0tCZWVjgIcWlMtLO1Yv3ADHE6ohchKc+QcCvvUB/KknThZqY+kCJGV1pnDVBM6BBzGkMeN\n6zmHo5SVMjmHuWSl42y+PAvgwKHlPvpjUjuO64U1cuQc+mDByhz6rxrz5w2Tc2gdx1ddBTz4oL2C\n7tyce7UyhznBoQBtC+Pij6/LKefQbydzTbPfA5WsMQdz6MuqLfd/VMyhtTons08dPOiKRuUCh7Oz\n7t/cXHrmkJGVMvLcNlmptZANw+RJu9Q5h5MsK52w7sYz1oHRVstkcw4ZsFAHGRqHThiCsqzyHlgA\nZSks4EfpUhWkqS8CbGEf7aLD5HwC66UwOZjDpSXXP608EeBlpQwDuCErXW8ynuW5M8whU/XPAg6Z\nojksOKwfyaJloADgllvc69VX69ts315V9GRyDq0O/Ctf6V6tFXQZcOiX5M8pKw2RNe7cma5/rNWL\nT6QuLOMXbbGAZQHnlpxDHxyOq6yUzd+XeSpKLQtzu2OHY/hzzBsAePGLbSBPnhsjKx0Vc8geZcES\nDSnB4aQzhxPW3XhmcWByykrZs+F8kKEZ8P7REtI+ZX6RtLXmHDLgnD0/iv1uMVjYnDmHwj5po9Qz\nM06KZ9lkfJBnGcuyoFplVpez1QtyMMwhE2hKDQ5Dcg7letYqmwcOuDaWcTUYuHFvCZCE5BwKOHzJ\nS/RtAOc8Arb7IWuC5VD0EGPAITP+cxsrh2cLy/jg0AJOLlfmkC1IIz6JtsKmb4cOAUeO5GEOAeDG\nG11hGq35stLUzOGoZaWpihsCcXIOrbnx42AT1t14llJWyhaWYStn1aNmVkdQwA3j0DFROiD9URax\nZKXanMO6rJRhii3g8OzZqo9W5tDqUDPMYR3AWmWlsllrJcuXs9WdY2tBGpY51AY5/LyposiTc+gz\nh9YjGBhwIXMgR87hDTcA3/u9NnYTAG6+2b1aweHJk3mZQ3H89+zRtZmEgjT1yoRW5tA6RkJlpZac\nwzpzaAWHDCt6+nQeWan4JEx+e25w+P732/OJl5fdOMlRkMYaDJNnJvuGtL1cmMMNWemEWsqoQdNg\nsoIFK8hjK3VZZY1NLFlK5pA9yqIODjUbGhshqt8T9pxDrVxn61bg+PGqnZU5tLIEUpDGssnUI+lW\n5nCDNawslDlkJeraIMfUVHVwsox7prgVkJ45ZE3ybnPkHE5NAb/yK1w/LTngQP6cw507bfcRmJxz\nDhkJmXy3EOaQPcrCAg4l5zaHrDS0II1VdSVAyLrfSN5yLnAoigKt+czh3r36drlkpaJe80EaU9OD\nrV9h8e8YUFlXQU0aOHzBxuXHVVbKMEkhoNJ6Pfa4B2D9YbpaJ7d+PQ2zMAlHWbCM77ZtYTmHDHNo\nLUjDSl/F8Z/ExTSV+Y4nk3PIStQtlYgZliAk51CqJ1pzDlmTOXD2rO6csZCcwxCzMu2jAIfz83ZZ\nqc8cjus5h8x6J9+NYdcWF+2BPqkWy+YcMrJSK6CPUZDG6jsxwchv+Rb3Oo7jERguSGOtVhqylrNH\nuQBpZaVsQRqWqawHRyyS5XGwFzQ4ZM7ZYwrSsOAwJ3PIVFRlrif3RStrqd9LLXPIFKRhZaUsm8pu\naHJwteVaAM8c7tkDnDjBg0NrzuG5c1wk93K1UTGHFhaKAYdtOYd93489kiXEfHA4O9v/+ZCcw5yW\nGxzOzQFnztirlcpakqs6pNV8AJVLVso8t127nIzYIiv15xvDHFqAKODGiFVWylb+9mWl1jn67ncP\nSyLHzWRMWmWlUrQoNXNYbwfklZVqgxYh4JAJ/IyLvaDBIQuGNDlvTM4hK0Wqy0MZ54yVlbLModap\ni5FzaPlujOyAzcMMyTnMyRwePOgO5bZsMiE5hxuy0mELzTlk1xK2sEYIc6gBv76zmktWOjPjcg4Z\ncDjOLPgowKGVOfSVElZHN5cJWAPs4CREVmoFy1deCTzzjJ4BB8KqlZ47Zw9GHjgAPPmkC0yxAXKr\nrPRy3G82bXLAdX7eDg5FspwaHDK+WixwyARaAZuMlcm5HRfbAIc91iQrTZVzyBaRYOWJDHMVIiv1\n76VWDhYr51Dz3fwINcAX1mCK+4SAQytzyILDZ591/9f2kck53JCVrrcYzGHqtYQFh/WCNJrv5+dA\n5c45XFjQg0M25zCnTYKs1D9ncnHRJpHLZX4frUoJhjkUWan1uV1xhQOHx47pz/r0weHysj3vnAGH\njz1m+14h4ESYw8txv9m3z91LppDNKMAhc5QFm3PIgkOrrHQDHE6YWaINoQVpmNK+1kIvocyhBRxK\nhSnr9erMoRYcMswtAw4FdPnfjalWyrCw1rw8RlYqEfjFRdtmPT3torj33w9cc42uTT0Hx8ocjrND\nndtCcw5D1hLmueVgDkcBDkUOqc05nIRKdaOSlV6O4HBx0f2fybmyjhFWVnrFFcCjj7r9Q87F7LMt\nWyoAZbn/Pji09HH/fpfGwBzRAXBF8i7X/ebqq4GHHrKDQ4Y5ZKtjx5CVMrmDIeBQm+O4ISudQGOr\nlWoL0oSAE7lODubQKn0aDKrKhNZ+yn0pS5ustJ5E3NfPeuKxJedTSvEDeZlDRh5qbTcYuEjik0/a\nHeqDB4F779WDQ5bdFFCplU++EMwH2uw5h8xaMimy0lwFaZ591jmsmjxMtiBTbpsEWemkgMOlpeow\ndSZX1yorXVy0y0qvuAL46lcda6jNlSuK4fw1Bhxacw7lulpj98SQnMNJsKuucvffMmd8WSkD8kJl\npRpwyKjX6n5hauZwQ1Y6oWY5hN1KYTcxh5rBwToULHMYyhJY+ymT+sIFdy2t1LN+wDbDHFpYOXkG\nqXMOWenfzAwXpQacU/DII/a8nf373av27LXQnMNch3JPgvn3kj3n0MpkAOllpU0FacZZVvr00zpJ\nKcAH+nIbK09k7XIHh/KstcCGLUjDgvoDB9yrljUUY8ChtLGu5XLvZI/TmB8wsjJXl2vOIeCCAYDL\nNdUaIytlz+hmgBdbG2JDVmqzDXDYY03ghIlsjCNzmDvnTSIplmi/n7sGcDmHlntZ/27WaqVray56\nzLAL2j7KAcjWKDXgwOEDD+gPoBZ7yUvcq+W5Md9NnvcGOKxMqscB6ZnD+mHe45hz6IOFXNVK9+wB\njh7lwOE4M4dXXukY0QsX8oCunTvt1Up9yea4g0MrWPNlpRZwInuA9XpyjS99Sd8G4MCh5Okya/nW\nrW6sWPrHFASSeXq5gsNjx9yrBRzmLEgTS1Y6jgVpJl1WOoFdjmNacOhv8oBeVsrkHLIO9dQUdwAv\nU5AG4A/8lQ3NslkwRWLYnEOAA75TU+vzHTSRY9aBlPtojVIDDhx+8YvAN36jvg0AfOQjwI//uP7z\ndXbZepSFtfz55Wx+QQhLziFz//122uAIMFxlNresNAc4vOIK4MEHnSxbY5PCHG7bBhw+7O5pDgdm\n9253lML27XpQ41cCHVdwuHWre85nztj6x8wbwB1JceoUd7THv/pXLkBoMVmDLGfmSSCAAYdf+pIb\nK9b+laU9RcPKkk2S/cAPAH/jb9jajLIgzeWWcyhkiOWcz3Gxy3A66EwLDv1EZ2l3uchKWcaRZQ4Z\ncNjEHPY9NwFMskix4NAiK2XkuawDKWX1LZXjxA4dAj76UeCOO2ztpqaAG2/Uf54dy1u2ONmZtYjB\n5Wz1UvKajaZeLVYLoOpyVMtZdKE5hzKH+uZBXVaaI4hwxRXA448D112n+zx7KPoo7JZb7EoC1vbu\ndXN7YcEuK11ddWNzHINGReH6efy4bU1mC9Ls3w88/zwnB37ve22fByr21nKUiFSmZcDhTTfZPj81\n5e6llQWU73W5Moe33+7+WWz7dvfcikL/3Py1XOtbA5N5lMXFi/p24qdNoi8zxltWWrOAQ4Y5ZGSN\n4mCtrYXJStkzzazSP7medlMTUJOaOQQqBmT79jDmUCsrZYp4hMpKLdIssSuucGMztTPIAt8NWel6\nq4NDDdBj739OWWk951B7htooqpWKLMsiK/XXyHF2PN/3vnxnBxaFAw3HjunXIHneAkzG9dDxHTsc\nYLPcS196ZhkjAg7LMo/juWePYyqtstInn8y3lgvjZd1Ll5ZsgOZytx07nNQc0B93UgeHWqaMIVJC\nZZ5iIeBQM8dlT2TY/XGwDXDYY3XmUJswy8hKB4MK1OSQebI5hz5gY5hDi0PHFKQBqgkt4DAlKxrC\nHC4suP9bo52S/2F1DG64wb3mBIcbBWnCbNs2O3PoB7WYQ7kBrkhVCHOo/W6jkpUCwCtfqfv8pOQc\nAnbZWajJmqfNla6Dw3E1YQ4tslJ/v7eMkbk5t0bKHpfa9u1z380yyd2rAAAgAElEQVQCDkNkpYyJ\nosCiJpCxtaFUqcwPvmnn26ZNw2fxhjCHKWWlbLVShqCQPXFSmcONgjQ95p8pJ+2sBWkY5iqHzLPO\nHFquxziQDHNYz/lkKlOxUk/LcSfsfWQcyMHALTYnTtidJZGGpgaHPqi3RGUl8LABDitjcg79dcta\n2p2pchqjII024jyKaqUyFrW5upOSczgKs1ShBIbB4TjmG4qxzKHs95YxUhQu7xDIw3hNAjiUccIU\nO9rIca/Mzy/VjmU/GKkFXQBfrZQ5yiJ3QRq5j5MKDsc4npnWtLkL27Y5OQXg5J5ra/2LOJtzKNez\n6uZZcFiXo1rYPLaQytGjdllpPeeQKeWfUlbKynpZBx5w9/L4cfuic/iwe9Uc5B1i/n20RGWnp+0B\nhMvdtm+v1iBLzmEIcwvkqVbKMoe5q5UCwN13A69+te6zk8Qc5rY/+iPuDLtJAIfWnEOpRMwURJmZ\ncWA0h+3d64KRuaqVMiZBI+t+s8EcDpt/Zqd2LMt6V5ZhstJJqFaq9QuFDNmQlU6YaQewHxGRQdi3\nsTUNeCs4SS2FlH6OIucw9CgLjTPIVsv0Fzntc2OZw7r0zwIOxTGwLjoy5g8etLWzmj8mLVFZyaXZ\nAIeVbd/uztgD9DmHIbLSkJxDbVQViAMOcxWkAYDXvEb/2Q3msN3e9jbb5wVAzc/bjjfIbdPTbu1i\nDhxnCqJo819j2L59wJEj7v9ax1+YQ8n9T207drhKuFu32iXLG8xhZcJ4ra7aZKWDgVvrcstKLYUD\nheAZDNKfc+gXDpzEwMOGrLTH/JxD7aCQgjRl6X5mZaU5mcMcTKVfrZRhKQFbKf8Q5nB11S0gmk0m\nhDlkwaE4IsyiU5b2anBWY5lDHxxO4oKawnxZqXbuxCpIY2UcLRthU0GacWYOLcaukRu23gYDdz+f\neMKtD+Nq+/cDjzxiZw6Zg+IB4Od/HnjTm2xtWNu3z1XrtXy32dn8OYcnTtj2DV9WurHfOJN7Yjm2\nBKiUUKllpSyTVxTDbbUglgWHwpxP6tjaAIc9Vpf+adoIqFhbcz+z4IQFGZbIfUh+EZC+WmmdOdQy\nJ2zulA/OtdGv3DmHQBg4zGH+GLE8b79E+0Yk15kPDhcWdJLg/7+9O4+S6yzvPP77SS0JLd0tdUuW\nJRnZSLYsLGPZYQ1g7NghgAGHHUMAkxBwYCALW8IQY2MmHJwhM+RkEhbbLDZhC2vYMmQwHhhyICy2\nAWO8yVqwhWxZ++IF6Zk/3nvpcrsl1Xur+9b2/ZzTp9RVdaveKlXXvc99nvd5G7+3qjakqfKdkNM0\nZPycw2YPKsqDzoh02YnZJMpKJ9e8eSnwanadyXY47jjp5z/PC6DK5kpVSs+e9jTpO9/J26aq0dH0\n2kZHm9+mHXMOc+fhN5aVsr9JhofT/1vuZ7JMpOT0GKhSZVc1WJMefFwy1XMOu72slODwCBozh1VL\npqoEDHWUlVZtLNNK5rDVpSyq/EFXeW2tzN2c6nUOpfTFc9ddnful01iem9M0ZNGiNG+H4HBM43dQ\ns3N+qn62Wi0rzZmTVLWsdNasNM59+1KpYdmYo5NQVjq5li2Trr++szOHxx2Xlm7ILSvNXVy+HU44\nIfULaLZbryQddZS0Y0f66eTMYVlW2qknWus2MpLKc6sEh/fdV3/mMOe4fHzmcKrLSskcdqEq6xzm\nZpNaCRjqCNZaaVpR5fnmzctfymJ85rDZstJW5xzWeSJAyu+6eNRR0oYNnful0zi/dObM5ueAzJqV\n/ua2bCE4LJUHkL/+dV4jrap/21XLSu+7L+8gt2pwKKWAcMeOFBwODze3TZ3IHE6uY46RrruuszOH\nxx6bLquUlXb6Mh0rVqS1PteubX6bgYGUady9u54TOFUbApXBeafuS+vWSnDYallplXUOc45BJys4\nzGlIQ3DYZZqtN64y51B6aMlmbnCSG0BNRnA41RnHMnO4a1fzk+mrZg4bx1h1zmeVSdVVu5Xu35/3\nRXz00fnzW+pUlhRVyQAuWpTmFxEcJo0HkHPnNtfpse6y0vFjbEbVOYeStGBBypzXtc5bLjKHk+uY\nY6TbbuvszGG5TFDOSb4qywa0gy297GXSWWflbbdzZ7qsY7mNoaG0eHvO+zht2lhHevY3ydy56bt4\n+/b2lJVWWcoi51itcYmtqcwcDg52d1lp357PrDLnsJVsUm5wknO2YTKCw1YaqTT7fPPnpy+c7dvT\nwV2zz1VlzmFjUD/V60yOf64q3Upzz1wefXRnZw5nzkzvQ5WdLsHhg1UJvOouK503L2V7p0/PyxxW\nmXMope+SDRtS1jBnWYS6VF3uBxM75ph02cmZwxNPTJfbtjW/TdmttNOX6ZCkSy/N3+b44x+8/55K\nCxdKP/lJ/j6x7HJafsb6nT128q1bGtLknMTMXY+x6vOVyZBuzUr37S6rarfSnMW8qzRtKQ/qqi4U\nX1dZaZXXNjqasklbtkgnndT8c1XJHFYN2KqUlU5GkF0lcyh19pfO0FD6v84d46JF0ne/29mvrU5l\ncNhsMxqp+lIW48tKc4LDdevS/Zst85yorLTZ79fG4LATDQyk+bYHDpA5nAzl+qzN7jfa5Z578k5q\nNWYOmz1h2k2+/e36nmt0NJ1UPPnkvO0GB9N+6vjjp2Zc3Wj+/BQc5nyWJ6OstJnvyonWvs4JDstO\n182Os7EKMOf5BgbS4+fOg+0UfVtWWmXOYe6HsOww2A2ZwzqWspg+PR34/+IXze8Ix38RNDvncHxQ\nP5Vlpa0GolJ+6UG5TmEnlysMD1ebO7hoUco4duISBe1QthavK3PYSsl4bkOaKosSS+ngZf36zg0O\n7WrNxTCxF7847ROPO67dIzm8kZHqc946+bu8qqGh+roJj46mhkC5B+KLF3d2FU47lJ/FnKqM8li5\nlbLSZko9q84BlB68DFIr3Uqbfb6q61F3AoLDI6iaOezG4HCql7KQUsbrxhvzykrHZw6nsqy03K7u\n8tzc0oOyBKbZg+l2aCVzKElLlkz+mLrRyEgKlnMyh+PnHFYpK835Tign3+cEh+N3ulXLSjtVleZi\nmFg5N6zXVCkZx8QWLsxbuL20eHE60URw2JrJyBw2Uz0yPmGQc6w2GcFhTj+QwcE0D7YbP1sEh0cw\nfp3D3MzhwYPp7Euz3RrLP7BumHNYlk3lHPgsWZLKFapmDquWleZkQPbty8sSV21a1Pg+5na0WrNG\n+uxnpec8p/lt6jY8nF+aIo0Fh2UpWb9bsCB15ty9u/kDyMaKh3vvbf6z1eoaprnZzfHlQTkNadat\n657gkMwhJtLrmcM6lWswVskc0pDmwd73Pul738vbZjLmHDazD2ic+iDlB4f796djwojmjgtbCQ6X\nLJFuvrk757P2dXCYG2RUKSvNPWs8OJgOArthzmG5TU7pQTlXrmrmsEpZaW5wuHdvfkOaql1f7713\n7DXlZhee//zODqDKzGHuTrfsZLts2eSPqRuV8/g2bcrLHFYpWa7aebf8u8lpyV/1xI8knXJKaj5R\nLh/QicrvoJzgHP2F4HDylMFh7vvYDfP363bGGdLjH5+3TavdSiOqZQ5zjsurrGvZSnB4/PHpdXXy\ncdqh9HVwmDvnMGcnX37p52bWhoZS++dOLittpVxqzZp0OTLS3P3LjoZlV8M6Moe5weH47HJOCWu5\nNlwv7pjKOYe5r23v3nRJKd6Y0dFURlllzmHOgWfjWdkqZaU56xy2Ehyefnra6Z5zTnP3b4fG96QX\n/77Rum7qVtrpjjoqXT7pSXnblfP3yRy2ptWy0gMHUoVdbkOa3Ole+/blnzBtnBeZk7hZuTIlQupY\n53Oy9W2xS5U5hzkfivJDmBscDg9Ld9yRf2bjwIFUwlpnWWmVRguvepX0pjc1HxyWjR0eeCCNL2fO\nYTnnM+c9KYPDnPe/auZwzpz0enbt6s2zxkNDqfQvd22y885Ln2WMWbgwb45d4/dW7o6wlbLS2bPz\ngsMqnYilVK7z7ndLZ57Z3P3boawCye1EjP5B5nDyzJ2bThjlesQj0mW5FAmqKY+DcrpOz5jx4DmA\nzWw3GXMOq1bTSHmZw5UruzNrKJE5PKLyg3jwYH5wuH9/3h+KlA7+ysxhs89ljx1o1VFWWv6BVZlL\nMzycvsBzGqk0Bnp1lZXmzp2qkjks1xO6447ePDBYskS69db8M7JLl0pvfvPUjKlbLVyYGjmVpVNH\n0jiftZWy0tyTKrlNc6pmDiXpbW/r7I62g4PpxA+ZQxxK+d24dWtv7gO6wTOekb7ruvUgvlM0lpU2\n+z3eWFba7HaTUVbaSoVLTnD4tKdJl1zS3H07DcHhETS2JM8J2MrgMHe+ydBQOqDI3a480KojOCzb\n1tfVaKFKh6k6g8OqmUMpZVDvuKM3Dx5Xr5Y2b+agZzIsXChde23zc+ymTatWQjN+ncNm/27KzOH2\n7c2X0LRSVtoNhobIHOLw7JRd2LBhbO4b6mUzhWEytNqQJrd7aJklrtKQJmef2Hh8J+UFhwsXdvbU\nh8OhrLQJ5YcjJ3NYfghzg7wyc2jnB4etZg5zSshy5+W1ouwgKk39UhZVgsPG7HLuPMwyOOzFg8ey\nTOdxj2vvOHpBeVY7pwHLyIi0bVv1stLcEulyjvVUdyLuFmVZKZlDHM6qVemEcLNVAUAnKtfjzS0r\nzQ0O7bHtypOZOY0i9+7NDw7LyjUpLzjsZmQOm1Cl61xj5jCnrK7MHOZkKaWxszZ1zDksswR1Zg7L\nJiWdmDlsZcHrkZG0cG8vBocrV6bLpz61vePoBY95TLrMDQ7vuiuvzKdqWem0aWOl7TlZyqpzDrsB\ncw7RjFWrpLVr2z0KoDXz56fERtWy0pygsvHEYrMJA6nanMPZs8eOJSWCw0lne6bty22vt73T9o9t\nP7247VjbB23vsr27uHz7uO0vtb3V9t223zPutmNtX217r+2f2z77SOPJCQ7LwKvKnMPcIG94uFpZ\naZldq6ustM7MYfkHHVFtzmHVlvw53eOqdnDt5bLSWbNSudRxx7V7JN3v0Y9Olw9/ePPbNGalm11u\nZvw6hzmf5XKHWeW5pPz52Z2OzCGa8ZznpEZtQDdbsCBNK6haVpoTVNYZHDZmDsvO+b20nzqUOstK\nByRtlHR6RGyy/UxJn7F9cnF7SBqOeGi/KdsXSDpX0qOKq/6P7XUR8aHi909K+q6kZ0h6pqTP2j4+\nIu451GCqZA5z5xzmrlcojS1lMX163gFFeSDSbPAktVZWumdPfQdzZeB74EDz8wMag8OcL4+qwWH5\nfFUyh9/5ztgSH71m+fJ2j6A3LFok3XRT3ndClaz0+Ix7zmd59ux0YqtZrTak6XSDg2PNxcgc4lCe\n+MR2jwBo3fz50o4dU19WWm5X7jtyjnmrzDmcOTOdKD1wYCxrmLO2d7eqLXMYEfsi4pKI2FT8/lVJ\nt0sqzonLhxnPKyT9XURsjojNkt4r6ZWSZHuVpNMkXRwR90XE5yX9RNLzDzeenA9iY1npVDekmT8/\nnX3Zty8vqJw3b+wsdbMf+qplpWVDmj17xhYsn0qNtezNBnnl/1lE3rIU5WurM3N47bVk13Bkq1bl\n3b9KcNjY/Cm3MiA3O8acQwDoDa1mDusqK82dc2iPHU/2S0mp1MY5h7YXS1ol6WfFVSFpve2Ntj9s\nu3F69hpJ1zf8fn1xnSSdJGldROw9xO0TqrKOXR1zDufNS39k69Y13xJeSgcie/bknaWuWlZaNqTJ\naVvfivKANecMUeOCrAMDzb+2GTPSfbdtqydzWAaFBIeYbFWCw8bmT7nBYe6SJRO1JO+l4LBq52kA\n6DZl5nDnzrz1eMtj0JygsnHfkXNcWI4xd13Rct4hweEUsz0g6eOSPhIRt0jaKumxko5VyiQOSvrn\nhk3mSdrZ8Puu4rqJbitvP2ROqzxT0YlzDsvnk9I6cc2qkjkcvwRDbuZw9+56gsPygLVKtrfK4sLz\n5klbttSTOXz849NlTqMRoBlVM4dl86fcz3KVzGEvN6QZGpJ+9av0mqb1bes3AP2grHrbvr35jtWN\n0xhy5xw2Zhxzlpa4++7848Jy3mE/BYe1L2Vh20qB4X2S3iBJRdbvx8Vd7rb9ekmbbc8tbtsjaajh\nYYaL6zTBbeXtuw81hgsvvFjTpkkXXyydeeaZOvPMMw875ipzDstgLTdzKElnnSXdckteXXM5DzA3\nOKwyv2jmzDS2bdvqyxzmzgMs5w7mZIhLIyPSpk31ZA7LUsGcRiNAMxYvlm69NS8r3VhWmvtZ/vCH\npdtvb/7+vV5WunKl9JOfMN8QQO8ry0pzljMqEy9SPZnDRYukrVurZQ7370/TlLo9OLzmmmt0zTXX\nHPF+7Vjn8ApJCyWdExEHDnO/0Fhm8wZJayX9sPj91OK68rYVDYGkivt+/FAP/Gd/drE+9rEUHDaj\nypzDkZH0h1KlpOjyy8cW+GxWlfkt5eRcKT9LMHduyq7VWVaaU8bauCRI7vu/cGFq/lElOMx9H6dN\nS8sNLFqUN0bgSFasSCc5Tjml+W1aKSs99dT006yBgbQ2aFm10GvB4apV6W974cJ2jwQAptbs2SlQ\nmz49vzeE1Nqcw2b3G6Oj0j33pMTByEhz2zSOMyI/2dNpxifE3vnOd054v1qLXWx/QNJqSedGxP0N\n1z/O9iono5L+XtK3IqLM/l0p6Y22l9peJumNkj4iSUVZ6nWSLrI9y/bzJJ0s6XOHGkduwNA45zAn\nONy2rVpZqZTfDalK5rCxhCynrLR8vs2b6y0r3b27+QY4w8NjnQJzg8NFi1Jdek5wWGYqc7Mt5fMB\nk23FinT5qEcd/n6Nyp3gwYP5Jzpy2Q8tD+ql4LA8QNq6tb3jAICpVh6zHjhcymecyZpz2GwwOmtW\nOh785S/zjl3LzCFlpVPA9nJJr5F0r6QtqbpUIemC4vLdkhYpzRf8d0kvLbeNiA/afoSknxb3vSwi\nLmt4+PMkfUzSdkkbJD3/cMtY5KaUG+ccNhtolMFhlbLSKgYHU7CWGxyWWYLcA7OhofQH9ohH5I81\n15w56bXlBIetZA7LYC3nzFIZnE/1ATXQrKVL0+Xq1c1vM23a2PyKOj7L5bzDhz2s94JDSbryyvS9\nBQC9riwtbVYrcw7vv39s3cGcE/ILF0rXXSedf37+OKdNIzicdBGxUYfPVH7qCNv/laS/Osxj/06z\nY6kyGTV3zuHISEpf1xUcVskczpiRMgQPPJAfRC1dKt18c15WoqoqZaUPe1j60tixIz84nD8/XeYs\nHVB2cM0txQOmyrRp6W/nCU/I227u3BTQ/PrXU78jrFoe1C1e/vJ2jwAA6rFoUfXgsErm8IEH8tcd\nXLRI+v738/o8lJnDgYH+CQ77soda1eAwJ9CbMyfVJ2/fXk8b87IBTs5SFvZYyWZucLhsWZqXV0dZ\n6eBgygLmZA7tVFq6ZUv++18u5J2zXdnBlQWv0Un27pVOPDFvmzlzUinknDlTv9hvrweHANAvcudX\ntzrnMKcZTWnlynSZExz24zqHfZnjyF2UuKyL3rs3L/AaGZHuuCMFUlNtaCjNsct9bWVWLjc4POaY\ntF2zwVoryknEOcGhNNZKPjdYq5LpLTOH+/Y1v8YP0InmzEmNVHLm3FY1axbBIQD0gquukm67rfn7\nt9qtNGcZi1JZ7dZsR1VpLHN4//317Bc7QV9mDnOzO7NmpW22bs07MzIyIt15Zz1lpUuWpDby5SLu\nzZozJ5Ve2s2ftZHGAt46ModlcJhTViqlIO1Xv8rPHF56qbRxY942ZeawyrqKQCdpzBxOtfGZw5zv\nIABA51ixQnrqU5u/f9U5hzNmVM8cPuUp6TKnKqYcZ85yat2uL4PD3AP4cpLt1q15nSUXL05nUeoI\nDo85Js0BzD2gmzs3va7cAGr58nT5yEfmbVfF6Ghq7lMlc1ilrHTu3Px1B8s5n5SVotvNnZsWCq5j\nJ1g2pJHIHAJAP5k1K33/R9SXOXziE/OXiiszh7kJim7Wt8FhbvOVW29NDU5yDphWr05dNo8/Pn+M\nuY46Kn85CikFMvfckx9APf3pKStaR0OaxsxhlbLSuuZ8lmWlBIfoZnWWlVZtSAAA6G7Tp6cmLw88\nkFc50jgHsI59RtnTg8xhj8s9gF+6VPrpT1PWMCcVXWbVchahrqoMCnfsyNuuLCHLDaCmT0+lrHUY\nHk5/lNu35521OeqoVGpbR3BIWSl6xdBQOqlVx+e4zLhLeWVFAIDuVwZ6OScHy5PxVTKHVcyfn3p6\n7N1L5rCnVQkON2zI78R00kmp/DJn4murnvSkvPvPnVstc1inadPSH+dNN+WtPbh6dSrrJXMING9k\nJK1hWscZ0sHBsXUAyRwCQH9pXA2g2WO18mR8XZnD4eEUHO7ZQ+awp+3enfcfXGbIcuYbSmni6xe/\nmLdNK266Sfr61/O2qVpWWrfRUemHP5R+67ea36bM3Oa28q+CzCF6xciItGlTe4JDGtIAQP8oO5bm\nLBVXVpzUdUJx/vxUlUdZaY/7xS/ySiLLNHK5PkqzBgak007L26YVq1blByZVy0rrVi5If8wxzW9T\nBofPetbkj2c8MofoFWXmsI7PcWNwmLucDgCgu1VZR7wMDutad7DMHPZTWWlfrnN47bXSa1+bt822\nbb25ft28eWleXqcHNJddJn3723lzPleulG68MZ31mWrz56fPSAQHuOhuo6PpLGmdmcMDB9JZ4Do6\nOwMAOkPV4LCcc1hn5nD6dDKHPW3jxjSPMMeCBWnuW69ZtCjNp+z0gGbxYumFL8zfbvXqyR/LRBYv\nTstmkDlEtyvn9daxEyy7wJVZw5yTPwCA7vawh6UM4P79zQeHjXMO684cEhz2uHIR93539NHS+vWd\nHxx2uvnz05mvbdsIDtHdyuCwzrJS1gcFgP4ze3YKuqqUleYElK3ox7LSvg0O6+wg2skWL05/ZASH\nrbHTe3nvvRzkoruNjqbLxYun/rnK4JCMOwD0n8HBdAya0620DA537apnuldZVkq30h73pS9RvlQq\nDwAJDltXLnVCO350s5Urpb/9W+lVr5r65yI4BID+VQZ6VTKHu3aldXmn2tBQKmHdsqWe5+sEfRkc\nnntuu0fQOY4+Ol3WNTevl23enC458YBuNmuW9Ja31LOsBMEhAPSvch+QExzOnZtKPOsKDqdNS93v\n9+2Tjj126p+vE/Rlt1KMKTOHz31ue8fRC/7kT/LXwgT6GcEhAPSvxnnnuZnDnTvzl5ir6rTTUglr\nLzamnAjBYZ+bPVu66SZp+fJ2j6T7XXRRu0cAdJfRUemeewgOAaAfVS0r3b27vsyhJJ1zTlr/t18Q\nHOI3C8wDQJ0WLpS2biU4BIB+NDgo3X13fnB44ECaylNXcPiCF9TzPJ2iTxKkAIBOMzIibd+ezgIT\nHAJAf2nsVtpscGinfhm33NI/DWLqRnAIAGiLGTPSzv3OOwkOAaDflCWiOUtZSCk4vP12gsOpQnAI\nAGibhQuljRsJDgGg31TpVipJS5akyzrWOexHBIcAgLYhOASA/tSYOZw1q/ntyowhHeKnBsEhAKBt\nFi6U1q1La1cBAPpH2ZBm5sy8ZSJ2706Xo6NTM65+R3AIAGibFSukG2+sb70qAEBnWLZMuu22FCTm\nuOIK6a67pmZMYCkLAEAbnXJKulyzpr3jAADUa+nStCzFCSfkbbdgwdSMBwmZQwBA26xdK02fLp14\nYrtHAgCo07Rp0iMewfd/pyE4BAC0zSmnSB/6UF4zAgBAb1ixQlq9ut2jQCNHRLvHUCvb0W+vGQAA\nAOg0X/tamnNO9rB+thURfsj1/RYoERwCAAAA6GeHCg4pKwUAAAAAEBwCAAAAAAgOAQAAAAAiOAQA\nAAAAiOAQAAAAACCCQwAAAACACA4BAAAAACI4BAAAAACI4BAAAAAAIIJDAAAAAIAIDgEAAAAAIjgE\nAAAAAIjgEAAAAAAggkMAAAAAgAgOAQAAAAAiOAQAAAAAiOAQAAAAACCCQwAAAACACA4BAAAAACI4\nBAAAAACI4BAAAAAAIIJDAAAAAIAIDgEAAAAAIjgEAAAAAIjgEAAAAAAggkMAAAAAgAgOAQAAAAAi\nOAQAAAAAiOAQAAAAACCCQwAAAACACA4BAAAAACI4BAAAAACI4BAAAAAAIIJDAAAAAIAIDgEAAAAA\nIjgEAAAAAKjG4ND2TNuX215ve6ftH9t+esPtZ9u+0fYe29+0vXzc9pfa3mr7btvvGXfbsbavtr3X\n9s9tn13X6wIAAACAXlBn5nBA0kZJp0fEsKQLJX3G9nLbo5I+J+ntkkYk/UjSp8sNbV8g6VxJj5J0\niqRn235Nw2N/sthmRNJfS/ps8ZgAALTFNddc0+4hAACQpbbgMCL2RcQlEbGp+P2rkm6X9GhJz5P0\ns4j4fETcL+liSWttryo2f4Wkv4uIzRGxWdJ7Jb1Skor7nCbp4oi4LyI+L+knkp5f12sDAGA8gkMA\nQLdp25xD24slnSDpBklrJF1f3hYR+yTdWlyv8bcX/y5vO0nSuojYe4jbW1Z1B98N23XDGKtuxxjb\nu103jLHqdt0wxqrbMcbJ2279+vW1PVc3bMcY27tdN4yx6nbdMMaq2zHG9m7XDWNsZbuJtCU4tD0g\n6eOSPhoRN0uaJ2nnuLvtkjRY/Hv87buK6ya6bfy2LeuW/2A+9O17rqrbdcMYq27XDWOsul03jLHq\ndoxx8rYjOGzfc1XdrhvGWHW7bhhj1e26YYxVt2OM7d2uG8bYynYTcURM2oM19YS2leYIzpP0+xFx\nwPb7JA1ExOsb7vdTSe+IiC/Y3iHpdyPih8Vtj5Z0dUQM236OpP8WESc3bPsPkg5GxJ9N8Pz1vmAA\nAAAA6DAR4fHXDbRhHFdIWijpnIg4UFx3g6TzyzvYnitppaSfNdy+VtIPi99PLa4rb1the25Daela\npczkQ0z0JgAAAABAv6u1rNT2ByStlnRu0Xim9AVJa2w/146bkGcAAA+GSURBVPYsSRdJui4ibilu\nv1LSG20vtb1M0hslfUSSivtcJ+ki27NsP0/SyUrdTwEAAAAATaitrLRYt3C9pHsllRnDkHRBRHzS\n9lmS/lHScknfl/TKiNjYsP17JL262OayiHjbuMf+mKTHS9og6XUR8a0pf1EAAAAA0CNqn3MIAAAA\nAOg8bVvKopfYXmD7C7b32L7d9kuK6x9p+we2t9m+x/Y3bD+y3ePtNbb/S/E+32v7w+NuO9v2jcX/\nzTeLLDMm0aHef9svtb3b9q7iZ6/tg7ZPa+d4e43tmbYvt73e9k7bP7b99Anu947i/T+rHeMEpsqh\n9sHFbS+y/fPib+Nntn+/nWPtRYfZBxxbfOfsatgXvL2dY+01h/v+t/344rjzHttbbH/a9tHtHnMv\nOdL+1/Yf276l+Ox/zfaSdo63WQSHk+OflMplF0l6maT3F0HgHZJeFBEjSk14vizpU20bZe+6Q9K7\nlJod/YbtUaW5p2+XNCLpR5I+Xfvoet+E739EfCIiBiNiKCKGJL1O0m0RcW07BtnDBiRtlHR6RAxL\nulDSZxpPhNheIekFku5szxCBKTXhPtj2UklXSfrz4m/jrZI+YXth+4bakybcBxRC0nDDvuBv6h1a\nzzvc9/8CSR+UdGzxs0dFvw5MmkO+/7bPlPQ3kp6tdAy6Xmm1ho5HWWmLbM+RtF3SSRFxW3HdxyTd\nERH/teF+A5IukHRpRMyb8MHQEtvvkrQsIv6o+P3Vks6PiCcXv8+RtFXSqcX6mphE49//CW6/WtK3\nIuJd9Y6s/9i+XtLFEfGF4vevS/p7Se+X9KqIuLqd4wMmy+H2wZK+KOlfI+LohvvfJenZEfH9doy3\nl02wDz5W0u2SZjR0p8cUG//933D9aZKuKYIYTJHy/Zf0REmzy2X6iqzhHZJWRsTt7RvhkZE5bN0q\nSQ+UO6XC9ZLWlL/Y3i5pn9LBGWfN6rNG6f9CkhQR+yTdqob/G9SjOEg4XanzMKaQ7cVK30s3FL+/\nUNK9EfFvbR0YMDUOtw/+gaQbbT/L9jSndZHvlfSTNoyzX4Wk9bY32v5wUdGDKVJ8/5+gseXeGp1x\niOsxSY7w/pcx18kT3NZRCA5bN0/SrnHX7ZI0WP4SEQskDUt6vRqCFUy5eZJ2jrvuQf83qM0rJH0n\nIja0eyC9rKhQ+Likj0TEzbYHlU5I/Wl7RwZMmUPugyOVRl2lVMp1n9LfxgURsb/eIfatrZIeq1TS\n+Gilfe8/t3VEPazh+/+j46ujbJ+iVPL45naMrR9M8P7/m6QX2j7Z9mxJ75B0UNKcNg6zKQSHrdsj\naWjcdcOSdjdeUeyMPijpSuY71Kap/xvU4uWSPtruQfQy21baMd0n6Q3F1RdJujIiNrVtYMDUOuT3\nvO2zJf2tpKdExAxJZ0q6ojhQxhSLiL0R8eOIOBgRdyudIP8923PbPbZec4jv//K24yV9TdIbIuI/\n2jC8njfR+x8R31QqL/28pHXFz25Jv2zPKJtHcNi6myUN2F7ZcN1aTZxSnq50xmBZHQODbpB0avlL\nsUNaKcoqamX7SZKWKDUHwtS5Qqnx1fMa5vecLelPbW+2vVnSw5Umy7+lXYMEJtnh9sGnSvq/ZROs\niPih0jrKv1v7KFEKcew5FSb6/i+ndPy7pHdGxCfaNbg+MOH7HxHvj4hVEbFEKUgckPSzNo2xafyB\ntqiYx/Z5SZfYnmP7yUqdia6y/bu2Ty3mOgxJ+h+Stkm6sY1D7jm2p9t+mFLwPWB7lu3pkr4gaY3t\n59qepZRFuY5mNJPrMO9/6XxJn4uIve0ZYe+z/QFJqyWdGxH3N9x0ltL8hrXFz52SXiPpH2sfJDAF\nDrcPVppz+GTba6XfNOR4sphzOKkOtQ+w/Tjbq5yMKvVd+FZEUL0ziQ71/W97maRvSvqHiLisXePr\ndYd5/2fZXlP8e7mkD0l6X0SMn+7UcehWOglsL5D0YUlPVaqx/8uI+LTtFyi1d14mab+k/5T0tojo\n+LMG3cT2RUqBX+OH+Z0RcYnTmm7/KGm50hnjV0bExjYMs2cd4f2fJWmz0tm0a9oxvl5X7HTWKzXa\nKM9YhtLcqk+Ou+86SX9Mt1L0kkPtg4vbXifpLyQdJeluSf8rIt7XrrH2okPtA5Syuu9WWmJkl1IG\n660RcVftg+xRh/v+V2qMcpGk8sSsJUWxtBQmwRHe/69J+rakFUrlpB+WdGF0QeBFcAgAAAAAoKwU\nAAAAAEBwCAAAAAAQwSEAAAAAQASHAAAAAAARHAIAAAAARHAIAAAAABDBIQAAAABABIcAAAAAABEc\nAgAAAABEcAgAAAAAEMEhAAAAAEAEhwAAAAAAERwCAAAAAERwCAAAAAAQwSEAAAAAQASHAAAAAAAR\nHAIAAAAARHAIAAAAABDBIQAAAABABIcAAAAAAPVIcGh7ve0ttmc3XPcq299q57gAAL2l2N/ss73T\n9jbb/8/2Bbbd7rEBANCqnggOJYXSa/nzCa4HAGCyhKRnRsSwpGMlvUfSX0q6oq2jAgBgEvRKcChJ\n/13Sm2wPjb/B9hNt/6ft7ba/b/u3i+tfZPsH4+77F7a/WNOYAQDdx5IUEbsj4iuSXizpfNsn2Z5p\n+722N9jebPufbM/6zYb279u+tsg83mL799r1IgAAGK+XgsMfSrpG0lsar7S9QNJXJL1P0qik/ynp\nq8X1X5a0yvbKhk1eIumf6xgwAKD7RcQPJP1S0ulKmcTjJZ1SXC6T9A5Jsv04SR+T9KYi8/gUSevb\nMGQAACbUS8GhJF0k6fW2Rxuue6akmyPiExFxMCI+JekXkp4dEfsl/atSQCjbJ0g6USloBACgWXcq\nnYB8jaS/iIidEbFXKVh8SXGfP5J0RURcLUkRsTkibm7LaAEAmEBPBYcRcYNSlvBtxVWWtFTShnF3\n3aB0NleSPqGxHfdLJX0xIu6d4qECAHrLMknTJc2R9KOiWc02SV9XChol6eGSbmvT+AAAOKKeCg4L\nF0t6tdKOOiTdIem4cfdZXlwvSf8uaZHttZLOUwoWAQBoiu3HKp2I/KKkfZLWRMRI8TO/KCGVpE2S\nVh7qcQAAaLeeCw4j4jZJn5b0p8VVX5d0gu3zbE+3/WJJj1TKMCoifi3pX5Qa2ixQChYBADgs24O2\nnyXpk5KuioifSrpc0vtsLyrus6yh6cwVkv7Q9u84WWr7xPaMHgCAh+qV4HD8khWXKJX2RERsk/Qs\nSW+WtLW4fGZxfemTks6W9JmIOFjDeAEA3evLtndK2qg0jeG9SvMJJemtkm6V9D3bOyR9Q9Iq6TeN\na/5QqUHaTqUmastrHTkAAIfhCJYCBAAAAIB+1yuZQwAAAABACwgOAQAAAAAEhwAAAAAAgkMAAAAA\ngAgOAQAAAADq0uDQ9kzbl9teb3un7R/bfnrD7WfbvtH2HtvftL284bYzbV9te4ftdYd5jjNsH7R9\nyVS/HgAAAABot64MDiUNKK0vdXpEDEu6UNJnbC+3PSrpc5LeLmlE0o8kfbph271KCxG/+VAPbntA\naR2q703N8AEAAACgs/TMOoe2r5d0saSFks6PiCcX18+RtFXSqRFxc8P9z5Z0WUSsmOCx/lLSAklH\nSfplRLxj6l8BAAAAALRPt2YOH8T2YkknSLpB0hpJ15e3RcQ+SbcW1zfzWMdK+kNJl0jypA8WAAAA\nADpQ1weHRQnoxyV9tMgMzpO0c9zddkkabPIh/17SXxdBJQAAAAD0ha4ODm1bKTC8T9Ibiqv3SBoa\nd9dhSbubeLxnSxqMiM9O5jgBAAAAoNMNtHsALbpCaY7hORFxoLjuBknnl3ewPVfSyuL6IzlL0qNt\nby5+H5b0a9uPiojnTt6wAQAAAKCzdG3m0PYHJK2WdG5E3N9w0xckrbH9XNuzJF0k6bqyGY2TWZJm\nSppme5btGcW2fy1plaS1xc+/SrpMaQ4iAAAAAPSsrgwOi3ULXyPpVElbbO+2vcv2SyJiq6TnS3q3\npG2SHiPpvIbNnyJpv6SvSHq4pH2S/rckRcTeiLir/CnutzcidtT12gAAAACgHXpmKQsAAAAAQHVd\nmTkEAAAAAEwugkMAAAAAAMEhAAAAAIDgEAAAAAAggkMAAAAAgAgOAQAAAAAiOAQAAAAAiOAQANDn\nbD/c9i7bbvdYAABoJ4JDAEDfsX277bMkKSI2RcRQRESNz3+G7U11PR8AAM0gOAQAoH6WVFswCgBA\nMwgOAQB9xfaVkpZL+kpRTvoW2wdtTytu/5btd9n+ru3dtr9ke8T2x23vtP1928sbHm+17W/Yvsf2\njbZf2HDbObZvKJ5nk+032p4j6WuSlhaPv8v20bYfa/s/bG+3fYftf7A90PBYB22/1vbNxTgusb2i\nGOcO258q719mJm2/zfbdttfZfmld7zEAoDsRHAIA+kpEvELSRknPjIghSZ/RQ7N4L5b0B5KWSjpe\n0n9IukLSAkm/kHSRJBWB3jckfVzSQknnSfon26uLx7lc0quL5zlZ0tURsU/SMyTdGRGDRUnrryQd\nkPTnkkYk/baksyS9bty4fk/SaZKeIOmtkj4o6aWSHi7pUZJe0nDfo4vHWirplZI+ZPuEzLcLANBH\nCA4BAP3qcA1oPhIR6yNit6SvS7otIr4VEQcl/YtSgCZJz5J0e0RcGcn1kj4nqcwe3i9pje3BiNgZ\nEdcd6gkj4scR8Z/F42yU9CFJZ4y726URsTcibpT0M0nfiIgNDeM8rfEhJV0YEQ9ExLclfVXSi478\ntgAA+hXBIQAAD7Wl4d/7J/h9XvHvYyU9wfa24me7UiZvcXH78yU9U9KGolz1CYd6Qtsn2P6y7c22\nd0j6G6VsZKO7mhyXJG2PiHsbft+glEUEAGBCBIcAgH40Wc1gNkm6JiJGip8FRZno6yUpIn4UEc+R\ntEjSl5RKWA/1/O+XdKOklRExX9Lbdfjs5pEssD274fflku5s4fEAAD2O4BAA0I9+JWlF8W+rehD2\nFUmrbL/M9oDtGbYfUzSpmWH7pbaHIuKApN1K8wqllPEbtT3U8FiDknZFxL5izuJrK46pZEnvLMZx\nulIG819afEwAQA8jOAQA9KP3SLrQ9jal0s/GTF7TWcWI2KPUJOY8pazcncVjzyzu8nJJtxdloq9R\nanKjiLhJ0iclrSvKUY+W9GZJf2B7l1KjmU+Nf7oj/D7eZknbizFdJemCiLi52dcGAOg/rnHNXwAA\nUAPbZ0i6KiKWH/HOAAAUyBwCAAAAAAgOAQAAAACUlQIAAAAAROYQAAAAACCCQwAAAACACA4BAAAA\nACI4BAAAAACI4BAAAAAAIIJDAAAAAICk/w8s+niHigWbQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ba6cf8cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data preparation for the training set will involve the following steps:\n",
    "\n",
    "1. Filter the original dataset to include only that time period reserved for the training set\n",
    "2. Scale the time series such that the values fall within the interval (0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create training set containing only the model features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (1416, 1)\n",
      "Test data shape:  (48, 1)\n"
     ]
    }
   ],
   "source": [
    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
    "\n",
    "print('Training data shape: ', train.shape)\n",
    "print('Test data shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale data to be in range (0, 1). This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 01:00:00</th>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 02:00:00</th>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 05:00:00</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 06:00:00</th>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 07:00:00</th>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 08:00:00</th>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 09:00:00</th>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-11-01 00:00:00  0.10\n",
       "2014-11-01 01:00:00  0.07\n",
       "2014-11-01 02:00:00  0.05\n",
       "2014-11-01 03:00:00  0.04\n",
       "2014-11-01 04:00:00  0.06\n",
       "2014-11-01 05:00:00  0.10\n",
       "2014-11-01 06:00:00  0.19\n",
       "2014-11-01 07:00:00  0.31\n",
       "2014-11-01 08:00:00  0.40\n",
       "2014-11-01 09:00:00  0.48"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "train['load'] = scaler.fit_transform(train)\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original vs scaled data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ba6e0d0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jpD9L+kYtY81ahcfiCkkvStog6T5Jc2oZa5YknS/pt5K2SrpxJ8tW/LtZT/+B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\noKm4yPEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ba6cf8588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also scale the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-30 00:00:00</th>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 01:00:00</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 02:00:00</th>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 03:00:00</th>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 04:00:00</th>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-12-30 00:00:00  0.33\n",
       "2014-12-30 01:00:00  0.29\n",
       "2014-12-30 02:00:00  0.27\n",
       "2014-12-30 03:00:00  0.27\n",
       "2014-12-30 04:00:00  0.30"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['load'] = scaler.transform(test)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement ARIMA method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An ARIMA, which stands for **A**uto**R**egressive **I**ntegrated **M**oving **A**verage, model can be created using the statsmodels library. In the next section, we perform the following steps:\n",
    "1. Define the model by calling SARIMAX() and passing in the model parameters: p, d, and q parameters, and P, D, and Q parameters.\n",
    "2. The model is prepared on the training data by calling the fit() function.\n",
    "3. Predictions can be made by calling the forecast() function and specifying the number of steps (horizon) which to forecast\n",
    "\n",
    "In an ARIMA model there are 3 parameters that are used to help model the major aspects of a times series: seasonality, trend, and noise. These parameters are:\n",
    "- **p** is the parameter associated with the auto-regressive aspect of the model, which incorporates past values. \n",
    "- **d** is the parameter associated with the integrated part of the model, which effects the amount of differencing to apply to a time series. \n",
    "- **q** is the parameter associated with the moving average part of the model.\n",
    "\n",
    "If our model has a seasonal component, we use a seasonal ARIMA model (SARIMA). In that case we have another set of parameters: P, D, and Q which describe the same associations as p,d, and q, but correspond with the seasonal components of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Forecasting horizon: 3 hours\n"
     ]
    }
   ],
   "source": [
    "# Specify the number of steps to forecast ahead\n",
    "HORIZON = 3\n",
    "print('Forecasting horizon:', HORIZON, 'hours')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting the best parameters for an Arima model can be challenging - somewhat subjective and time intesive, so we'll leave it as an exercise to the user. We used an **auto_arima()** function and some additional manual selection to find a decent model.\n",
    "\n",
    ">NOTE: For more info on selecting an Arima model, please refer to the an arima notebook in /ReferenceNotebook directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 Statespace Model Results                                 \n",
      "==========================================================================================\n",
      "Dep. Variable:                               load   No. Observations:                 1416\n",
      "Model:             SARIMAX(4, 1, 0)x(1, 1, 0, 24)   Log Likelihood                3477.240\n",
      "Date:                            Mon, 08 Oct 2018   AIC                          -6942.479\n",
      "Time:                                    12:56:56   BIC                          -6911.053\n",
      "Sample:                                11-01-2014   HQIC                         -6930.728\n",
      "                                     - 12-29-2014                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.8406      0.016     52.084      0.000       0.809       0.872\n",
      "ar.L2         -0.5230      0.034    -15.384      0.000      -0.590      -0.456\n",
      "ar.L3          0.1531      0.044      3.461      0.001       0.066       0.240\n",
      "ar.L4         -0.0785      0.036     -2.178      0.029      -0.149      -0.008\n",
      "ar.S.L24      -0.2349      0.024     -9.831      0.000      -0.282      -0.188\n",
      "sigma2         0.0004   8.32e-06     47.353      0.000       0.000       0.000\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       90.44   Jarque-Bera (JB):              1460.40\n",
      "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               0.84   Skew:                             0.14\n",
      "Prob(H) (two-sided):                  0.07   Kurtosis:                         8.01\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "order = (4, 1, 0)\n",
    "seasonal_order = (1, 1, 0, 24)\n",
    "\n",
    "model = SARIMAX(endog=train, order=order, seasonal_order=seasonal_order)\n",
    "results = model.fit()\n",
    "\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we display the distribution of residuals. A zero mean in the residuals may indicate that there is no bias in the prediction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will perform the so-called **walk forward validation**. In practice, time series models are re-trained each time a new data becomes available. This allows the model to make the best forecast at each time step. \n",
    "\n",
    "Starting at the beginning of the time series, we train the model on the train data set. Then we make a prediction on the next time step. The prediction is then evaluated against the known value. The training set is then expanded to include the known value and the process is repeated. (Note that we keep the training set window fixed, for more efficient training, so every time we add a new observation to the training set, we remove the observation from the beginning of the set.)\n",
    "\n",
    "This process provides a more robust estimation of how the model will perform in practice. However, it comes at the computation cost of creating so many models. This is acceptable if the data is small or if the model is simple, but could be an issue at scale. \n",
    "\n",
    "Walk-forward validation is the gold standard of time series model evaluation and is recommended for your own projects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CU7dhfI950BOcC1p4yRczrEceeSR9RPZsvfXW3piuNFQ0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\njzGn3nR4Yp191zFm/iX7mboN4nuCN53Y35xzzxx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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('./images/ts_cross_validation.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a test data point for each HORIZON step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>load+1</th>\n",
       "      <th>load+2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-30 00:00:00</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 01:00:00</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 02:00:00</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 03:00:00</th>\n",
       "      <td>0.27</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30 04:00:00</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load  load+1  load+2\n",
       "2014-12-30 00:00:00  0.33    0.29    0.27\n",
       "2014-12-30 01:00:00  0.29    0.27    0.27\n",
       "2014-12-30 02:00:00  0.27    0.27    0.30\n",
       "2014-12-30 03:00:00  0.27    0.30    0.41\n",
       "2014-12-30 04:00:00  0.30    0.41    0.57"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_shifted = test.copy()\n",
    "\n",
    "for t in range(1, HORIZON):\n",
    "    test_shifted['load+'+str(t)] = test_shifted['load'].shift(-t, freq='H')\n",
    "    \n",
    "test_shifted = test_shifted.dropna(how='any')\n",
    "test_shifted.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make predictions on the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-12-30 00:00:00\n",
      "1 : predicted = [0.32 0.29 0.28] expected = [0.32945389435989236, 0.2900626678603402, 0.2739480752014323]\n",
      "2014-12-30 01:00:00\n",
      "2 : predicted = [0.3  0.29 0.3 ] expected = [0.2900626678603402, 0.2739480752014323, 0.26812891674127126]\n",
      "2014-12-30 02:00:00\n",
      "3 : predicted = [0.27 0.28 0.32] expected = [0.2739480752014323, 0.26812891674127126, 0.3025962399283795]\n",
      "2014-12-30 03:00:00\n",
      "4 : predicted = [0.28 0.32 0.42] expected = [0.26812891674127126, 0.3025962399283795, 0.40823634735899716]\n",
      "2014-12-30 04:00:00\n",
      "5 : predicted = [0.3  0.39 0.54] expected = [0.3025962399283795, 0.40823634735899716, 0.5689346463742166]\n",
      "2014-12-30 05:00:00\n",
      "6 : predicted = [0.4  0.56 0.67] expected = [0.40823634735899716, 0.5689346463742166, 0.6799462846911368]\n",
      "2014-12-30 06:00:00\n",
      "7 : predicted = [0.57 0.68 0.75] expected = [0.5689346463742166, 0.6799462846911368, 0.7309758281110115]\n",
      "2014-12-30 07:00:00\n",
      "8 : predicted = [0.68 0.75 0.8 ] expected = [0.6799462846911368, 0.7309758281110115, 0.7511190689346463]\n",
      "2014-12-30 08:00:00\n",
      "9 : predicted = [0.75 0.8  0.82] expected = [0.7309758281110115, 0.7511190689346463, 0.7636526410026856]\n",
      "2014-12-30 09:00:00\n",
      "10 : predicted = [0.76 0.78 0.78] expected = [0.7511190689346463, 0.7636526410026856, 0.7381378692927483]\n",
      "2014-12-30 10:00:00\n",
      "11 : predicted = [0.76 0.75 0.74] expected = [0.7636526410026856, 0.7381378692927483, 0.7188898836168307]\n",
      "2014-12-30 11:00:00\n",
      "12 : predicted = [0.77 0.76 0.75] expected = [0.7381378692927483, 0.7188898836168307, 0.7090420769919425]\n",
      "2014-12-30 12:00:00\n",
      "13 : predicted = [0.7  0.68 0.69] expected = [0.7188898836168307, 0.7090420769919425, 0.7081468218442255]\n",
      "2014-12-30 13:00:00\n",
      "14 : predicted = [0.72 0.73 0.76] expected = [0.7090420769919425, 0.7081468218442255, 0.7385854968666068]\n",
      "2014-12-30 14:00:00\n",
      "15 : predicted = [0.71 0.73 0.86] expected = [0.7081468218442255, 0.7385854968666068, 0.8478066248880931]\n",
      "2014-12-30 15:00:00\n",
      "16 : predicted = [0.73 0.85 0.97] expected = [0.7385854968666068, 0.8478066248880931, 0.9516562220232765]\n",
      "2014-12-30 16:00:00\n",
      "17 : predicted = [0.87 0.99 0.97] expected = [0.8478066248880931, 0.9516562220232765, 0.934198746642793]\n",
      "2014-12-30 17:00:00\n",
      "18 : predicted = [0.94 0.92 0.86] expected = [0.9516562220232765, 0.934198746642793, 0.8876454789615038]\n",
      "2014-12-30 18:00:00\n",
      "19 : predicted = [0.94 0.89 0.82] expected = [0.934198746642793, 0.8876454789615038, 0.8294538943598924]\n",
      "2014-12-30 19:00:00\n",
      "20 : predicted = [0.88 0.82 0.71] expected = [0.8876454789615038, 0.8294538943598924, 0.7197851387645477]\n",
      "2014-12-30 20:00:00\n",
      "21 : predicted = [0.83 0.72 0.58] expected = [0.8294538943598924, 0.7197851387645477, 0.5747538048343777]\n",
      "2014-12-30 21:00:00\n",
      "22 : predicted = [0.72 0.58 0.47] expected = [0.7197851387645477, 0.5747538048343777, 0.4592658907788718]\n",
      "2014-12-30 22:00:00\n",
      "23 : predicted = [0.58 0.47 0.39] expected = [0.5747538048343777, 0.4592658907788718, 0.3858549686660697]\n",
      "2014-12-30 23:00:00\n",
      "24 : predicted = [0.46 0.38 0.34] expected = [0.4592658907788718, 0.3858549686660697, 0.34377797672336596]\n",
      "2014-12-31 00:00:00\n",
      "25 : predicted = [0.38 0.34 0.33] expected = [0.3858549686660697, 0.34377797672336596, 0.32542524619516544]\n",
      "2014-12-31 01:00:00\n",
      "26 : predicted = [0.36 0.34 0.34] expected = [0.34377797672336596, 0.32542524619516544, 0.33034914950760963]\n",
      "2014-12-31 02:00:00\n",
      "27 : predicted = [0.32 0.32 0.35] expected = [0.32542524619516544, 0.33034914950760963, 0.3706356311548791]\n",
      "2014-12-31 03:00:00\n",
      "28 : predicted = [0.32 0.36 0.47] expected = [0.33034914950760963, 0.3706356311548791, 0.470008952551477]\n",
      "2014-12-31 04:00:00\n",
      "29 : predicted = [0.37 0.48 0.65] expected = [0.3706356311548791, 0.470008952551477, 0.6145926589077886]\n",
      "2014-12-31 05:00:00\n",
      "30 : predicted = [0.48 0.64 0.75] expected = [0.470008952551477, 0.6145926589077886, 0.7247090420769919]\n",
      "2014-12-31 06:00:00\n",
      "31 : predicted = [0.63 0.73 0.79] expected = [0.6145926589077886, 0.7247090420769919, 0.786034019695613]\n",
      "2014-12-31 07:00:00\n",
      "32 : predicted = [0.71 0.76 0.79] expected = [0.7247090420769919, 0.786034019695613, 0.8012533572068039]\n",
      "2014-12-31 08:00:00\n",
      "33 : predicted = [0.78 0.82 0.83] expected = [0.786034019695613, 0.8012533572068039, 0.7994628469113696]\n",
      "2014-12-31 09:00:00\n",
      "34 : predicted = [0.82 0.83 0.81] expected = [0.8012533572068039, 0.7994628469113696, 0.780214861235452]\n",
      "2014-12-31 10:00:00\n",
      "35 : predicted = [0.8  0.78 0.76] expected = [0.7994628469113696, 0.780214861235452, 0.7587287376902416]\n",
      "2014-12-31 11:00:00\n",
      "36 : predicted = [0.77 0.75 0.74] expected = [0.780214861235452, 0.7587287376902416, 0.7367949865711727]\n",
      "2014-12-31 12:00:00\n",
      "37 : predicted = [0.77 0.76 0.76] expected = [0.7587287376902416, 0.7367949865711727, 0.7188898836168307]\n",
      "2014-12-31 13:00:00\n",
      "38 : predicted = [0.75 0.75 0.78] expected = [0.7367949865711727, 0.7188898836168307, 0.7273948075201431]\n",
      "2014-12-31 14:00:00\n",
      "39 : predicted = [0.73 0.75 0.87] expected = [0.7188898836168307, 0.7273948075201431, 0.8299015219337511]\n",
      "2014-12-31 15:00:00\n",
      "40 : predicted = [0.74 0.85 0.96] expected = [0.7273948075201431, 0.8299015219337511, 0.909579230080573]\n",
      "2014-12-31 16:00:00\n",
      "41 : predicted = [0.83 0.94 0.93] expected = [0.8299015219337511, 0.909579230080573, 0.855863921217547]\n",
      "2014-12-31 17:00:00\n",
      "42 : predicted = [0.94 0.93 0.88] expected = [0.909579230080573, 0.855863921217547, 0.7721575649059982]\n",
      "2014-12-31 18:00:00\n",
      "43 : predicted = [0.87 0.82 0.77] expected = [0.855863921217547, 0.7721575649059982, 0.7023276633840643]\n",
      "2014-12-31 19:00:00\n",
      "44 : predicted = [0.79 0.73 0.63] expected = [0.7721575649059982, 0.7023276633840643, 0.6195165622202325]\n",
      "2014-12-31 20:00:00\n",
      "45 : predicted = [0.7  0.59 0.46] expected = [0.7023276633840643, 0.6195165622202325, 0.5425246195165621]\n",
      "2014-12-31 21:00:00\n",
      "46 : predicted = [0.6  0.47 0.36] expected = [0.6195165622202325, 0.5425246195165621, 0.4735899731423454]\n",
      "CPU times: user 26min 45s, sys: 12min 48s, total: 39min 33s\n",
      "Wall time: 7min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "training_window = 720 # dedicate 30 days (720 hours) for training\n",
    "\n",
    "train_ts = train['load']\n",
    "test_ts = test_shifted\n",
    "\n",
    "history = [x for x in train_ts]\n",
    "history = history[(-training_window):]\n",
    "\n",
    "predictions = list()\n",
    "\n",
    "# let's user simpler model for demonstration\n",
    "order = (2, 1, 0)\n",
    "seasonal_order = (1, 1, 0, 24)\n",
    "\n",
    "for t in range(test_ts.shape[0]):\n",
    "    model = SARIMAX(endog=history, order=order, seasonal_order=seasonal_order)\n",
    "    model_fit = model.fit()\n",
    "    yhat = model_fit.forecast(steps = HORIZON)\n",
    "    predictions.append(yhat)\n",
    "    obs = list(test_ts.iloc[t])\n",
    "    # move the training window\n",
    "    history.append(obs[0])\n",
    "    history.pop(0)\n",
    "    print(test_ts.index[t])\n",
    "    print(t+1, ': predicted =', yhat, 'expected =', obs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare predictions to actual load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>h</th>\n",
       "      <th>prediction</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-12-30 00:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,008.79</td>\n",
       "      <td>3,023.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-12-30 01:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,955.40</td>\n",
       "      <td>2,935.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-12-30 02:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,900.11</td>\n",
       "      <td>2,899.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-12-30 03:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,917.93</td>\n",
       "      <td>2,886.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-12-30 04:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,946.76</td>\n",
       "      <td>2,963.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp    h  prediction   actual\n",
       "0 2014-12-30 00:00:00  t+1    3,008.79 3,023.00\n",
       "1 2014-12-30 01:00:00  t+1    2,955.40 2,935.00\n",
       "2 2014-12-30 02:00:00  t+1    2,900.11 2,899.00\n",
       "3 2014-12-30 03:00:00  t+1    2,917.93 2,886.00\n",
       "4 2014-12-30 04:00:00  t+1    2,946.76 2,963.00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
    "eval_df['timestamp'] = test.index[0:len(test.index)-HORIZON+1]\n",
    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
    "eval_df['actual'] = np.array(np.transpose(test_ts)).ravel()\n",
    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the **mean absolute percentage error (MAPE)** over all predictions\n",
    "\n",
    "$$MAPE = \\frac{1}{n} \\sum_{t=1}^{n}|\\frac{actual_t - predicted_t}{actual_t}|$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h\n",
      "t+1   0.01\n",
      "t+2   0.01\n",
      "t+3   0.02\n",
      "Name: APE, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "if(HORIZON > 1):\n",
    "    eval_df['APE'] = (eval_df['prediction'] - eval_df['actual']).abs() / eval_df['actual']\n",
    "    print(eval_df.groupby('h')['APE'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One step forecast MAPE:  0.5555183537755334 %\n"
     ]
    }
   ],
   "source": [
    "print('One step forecast MAPE: ', (mape(eval_df[eval_df['h'] == 't+1']['prediction'], eval_df[eval_df['h'] == 't+1']['actual']))*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multi-step forecast MAPE:  1.1433392660923376 %\n"
     ]
    }
   ],
   "source": [
    "print('Multi-step forecast MAPE: ', mape(eval_df['prediction'], eval_df['actual'])*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the predictions vs the actuals for the first week of the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UJ38nDBISpBhQv37OsY4Ziextk0EUUv4vk6P48Kc0uPPOsI1LKaVU8xLcG3Pg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\nQ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YuLH2iVjVdHqKz0Jso2NfmbTUSmtgVfve4D+zKHUiHe35v2OZVMRydOwOCg92GMJTJ6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\nERGRsFHIFBERERERkbD5/zq+2mM3gq6qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7ba6a324e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if(HORIZON == 1):\n",
    "    ## Plotting single step forecast\n",
    "    eval_df.plot(x='timestamp', y=['actual', 'prediction'], style=['r', 'b'], figsize=(15, 8))\n",
    "\n",
    "else:\n",
    "    ## Plotting multi step forecast\n",
    "    plot_df = eval_df[(eval_df.h=='t+1')][['timestamp', 'actual']]\n",
    "    for t in range(1, HORIZON+1):\n",
    "        plot_df['t+'+str(t)] = eval_df[(eval_df.h=='t+'+str(t))]['prediction'].values\n",
    "\n",
    "    fig = plt.figure(figsize=(15, 8))\n",
    "    ax = plt.plot(plot_df['timestamp'], plot_df['actual'], color='red', linewidth=4.0)\n",
    "    ax = fig.add_subplot(111)\n",
    "    for t in range(1, HORIZON+1):\n",
    "        x = plot_df['timestamp'][(t-1):]\n",
    "        y = plot_df['t+'+str(t)][0:len(x)]\n",
    "        ax.plot(x, y, color='blue', linewidth=4*math.pow(.9,t), alpha=math.pow(0.8,t))\n",
    "    \n",
    "    ax.legend(loc='best')\n",
    "    \n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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